Genetic testing is widely used in diagnosing genetic hearing loss in patients. Other than providing genetic etiology, the benefits of genetic testing in pediatric patients with hearing loss are less investigated.GJB2/SLC26A4 multiplex amplicon sequencing, STRC/OTOA copy number variation analysis, and exome sequencing, were hierarchically offered. Clinical follow-up and examinations were performed.From 2018–2020, pediatric patients who initially presented isolated hearing loss were enrolled. Comprehensive genetic testing, including A total of 80 pediatric patients who initially presented isolated hearing loss were considered as nonsyndromic hearing loss and enrolled in this study. The definitive diagnosis yield was 66% (53/80) and the likely diagnosis yield was 8% (6/80) through comprehensive genetic testing. With the aid of genetic testing and further clinical follow-up and examinations, the clinical diagnoses and medical management were altered in eleven patients ; five were syndromic hearing loss; six were nonsyndromic hearing loss mimics.Syndromic hearing loss and nonsyndromic hearing loss mimics are common in pediatric patients who initially present with isolated hearing loss. The comprehensive genetic testing provides not only a high diagnostic yield but also valuable information for clinicians to uncover subclinical or pre-symptomatic phenotypes, which allows early diagnosis of SHL, and leads to precise genetic counseling and changes the medical management.The online version contains supplementary material available at 10.1186/s12920-022-01293-x. Childhood hearing loss affects 1 to 5 per 1,000 newborns , 3. To dEarly genetic diagnosis of SHL would significantly reduce other testing and provide opportunities for early intervention . HoweverThe SHL genes can be identified in NSHL patients due to genetic heterogeneity . Of 102 With the advent of next-generation sequencing techniques, a growing number of clinical laboratories implemented genetic testing (hearing loss panel or exome sequencing) to uncover the genetic etiology of hearing loss . ConsensIn this study, we enrolled 80 pediatric outpatients who initially presented isolated hearing loss and were clinically diagnosed as NSHL. Our comprehensive genetic testing achieved a high definitive diagnostic yield (66%) and likely diagnostic yield (8%). With the aid of genetic testing, following clinical follow-up and examinations, the clinical diagnosis of 11 (19%) molecular diagnosed patients was altered from NSHL to SHL or NSHL mimics. More importantly, they were referred to new specialists and more specific genetic counseling and medical management was conducted.This study was approved by the institutional review boards at Tongji Medical College of Huazhong University of Science and Technology.Between 2018 and 2020, pediatric patients who met the following criteria were recruited at Tongji Medical College of Huazhong University of Science and Technology: (1) outpatients who initially presented isolated hearing loss at the Department of Ear Nose & Throat; (2) bilateral hearing loss; (3) prelingual onset.GJB2 and SLC26A4 gene in prelingual hearing loss in the East Asian population [GJB6 deletion analysis was suggested using low-pass genome sequencing [STRC/OTOA plays a significant role in moderate hearing loss [GJB2 or SLC26A4 gene were referred to STRC/OTOA analysis . Patients with severe or profound hearing loss or negative for STRC/OTOA analysis were referred to exome sequencing. Exome sequencing was completed using KAPA HyperExome Probes  accompanied by 100-bp paired-end sequencing on an MGISEQ-2000 platform . CNVs were called from WES data using ExomeDepth software version 1.0.7.18 [Considering the significant contribution of the pulation , the twopulation .When susquencing . Becauseing loss , these p1.0.7.18 . The iniSequence variants were interpreted based on the expert specifications of variant interpretation guidelines for genetic hearing loss . SpecifiAll reported sequence variants were confirmed via Sanger sequencing (single nucleotide variants), qPCR (exon-level copy number variations (CNV)), or low-pass genome sequencing . Family segregation analysis was performed.The positive genotype was defined as follows: (1) patients harboring pathogenic/likely pathogenic variants consistent with the inheritance pattern and segregating with hearing loss was defined as definitive diagnosis; (2) patients with pathogenic/likely pathogenic variants in trans with a variant of uncertain significance in autosomal recessive condition was defined as likely diagnosis.When variants in SHL genes were identified in patients with isolated hearing loss, clinical follow-ups were conducted. Patients were recalled for further clinical examinations, which included but were not limited to, audiometry, physical examination, otoscopy, ophthalmoscopy, developmental assessment, and electrocardiogram analysis. The clinical follow-up ended in September 2021.Finally, SHL was clinically diagnosed when phenotypes other than the hearing loss were uncovered by the date of clinical follow-up. If the genotypes were reported to be intensively associated with syndromic conditions in public literature, which did not present in the pediatric patients by the date of clinical follow-up, we define these patients as NSHL mimics.Between January 2018 and December 2020, a total of 80 patients with isolated, prelingual, bilateral hearing loss were enrolled. Overall, 73% (n = 58), 7% (n = 6), 19% (n = 15), 1% (n = 1) of patients were diagnosed with profound, severe, moderate, and mild sensorineural hearing loss, respectively. Most patients  reported no family history of hearing loss. Of 73 patients who received the newborn hearing screening at birth, 88% (64/73) were referred for audiological evaluations, and 12% (9/73) passed the hearing screening program :c.299_300delAT and del(GJB6-D13S1854)  child  pediatric patients and likely genetic etiology was confirmed in 6 out of 80 (8%) , one STRC homozygous deletion (P59), two exon-level CNVs (P54 and P62). Five (8%) patients harbored 7 novel variants, which were not reported either in literature or public databases. The autosomal recessive inheritance and autosomal dominant inheritance accounted for 86% (51/59) and 14% (8/59), respectively  patients, including one subchromosomal CNV (P10), one Prior to genetic testing, the enrolled pediatric probands presented isolated hearing loss, which led to the clinical diagnosis of NSHL. However, genetic testing identified patients harboring variants in genes associated with SHL. These patients were referred to new specialists, and clinical follow-up and examinations were performed. As a result, phenotypes other than hearing loss were identified in five patients , suggesting a clinical diagnosis of SHL  in MYO7A were identified in compound heterozygous in trans. MYO7A is associated with Usher syndrome, type 1B (OMIM #276900), an autosomal recessive disorder characterized by retinitis pigmentosa, vestibular dysfunction, and hearing loss [In proband 8, a frameshift variant (c.3696_3706del) in ing loss . Clinicaing loss , mild deIn proband 10, a 3.7 Mb heterozygous deletion in the chromosome 17p11.2 region was detected by exome sequencing, which was confirmed by low-pass genome sequencing. The 17p11.2 deletion causes Smith-Magenis syndrome (OMIM# 182290). The clinical features include distinctive craniofacial and skeletal features, global developmental delay, cognitive impairment, mental retardation, as well as hearing loss . FurtherCREBBP (c.3699-1G>A). The loss of function variant of CREBBP is causative for Rubinstein-Taybi syndrome (OMIM# 180849) or Menke-Hennekam syndrome 1(OMIM# 618332) [In proband 13, exome sequencing detected a novel splicing variant in  618332) . FurtherEYA1 was identified. It was inherited from his mother and maternal grandmother. Branchiootic syndrome 1 (OMIM #602588) caused by EYA1 variants showed a variable spectrum of manifestations, including hearing loss, branchial fistulae, preauricular pits, and renal abnormalities [In proband 62, a novel heterozygous variant (Exon 8–9 del) in malities . AlthougVariants related to SHL genes were identified in six probands , whereas clinical phenotypes other than hearing loss were not observed, indicating a clinical diagnosis of NSHL mimics , a heterozygous variant in MITF (c.877C>T) were identified in proband 33, 42, and 72 respectively. The reported variants caused Keratitis-ichthyosis-deafness (KID) syndrome (OMIM #148210), Muenke syndrome (OMIM #602849), and Waardenburg syndrome, type 2A (OMIM #193510), respectively. However, the three probands did not have additional clinical phenotypes after clinical examinations by the time of the latest follow-up.More specifically, two linked heterozygous variants (c.109G>A and c.263C>T) in KCNQ1 (c.1684A>G and Exon 8–9 del) were identified by exome sequencing in proband 54. KCNQ1 is associated with Jervell and Lange-Nielsen syndrome , which is characterized by profound congenital hearing loss and a prolonged QTc interval in the electrocardiography waveforms [Additionally, two variants in compound heterozygote in aveforms , paddlesMITF were identified by exome sequencing, respectively, which led to the genetic diagnosis of Waardenburg syndrome, type 2A (OMIM# 193510). The missense variant in proband 38 was inherited from the hearing-impaired father and paternal grandmother. However, only the father demonstrated premature graying of hair during clinical follow-ups. The c.877C>T was identified in P43. Clinical follow-up revealed that the mother and maternal grandmother had a slight white forelock. The family members did not consider the abnormal pigmentation as a severe phenotype, thus did not report to clinicians in the recruitment.In proband 38 and 43, a missense variant (c.641G>A) and a nonsense variant (c.877C>T) and in Childhood hearing loss is one of the most heterogeneous conditions. Early identification of hearing loss and understanding its etiology can assist with the prognosis and counseling of families . In thisGJB2 and SLC26A4 gene in the East Asian population [GJB6 deletion compound with a GJB2 variant, one STRC homozygous deletion, two exon-level CNVs. Nevertheless, the genetic spectrum reinforces the genetic heterogeneity of hearing loss, and a comprehensive genetic testing approach including exome sequencing, MLPA, low-pass genome sequencing is warranted.Our tiered-based genetic testing strategy has a definitive and likely diagnostic yield of 74% in pediatric patients with prelingual hearing loss. It is higher than the previously reported rate ranging from 40 – 65% . This mipulation  and the GJB2-mutation-negative children who are clinically identified as NSHL harbor pathogenic variants in genes associated with SHL [SHL gene are commonly identified in children presenting with isolated hearing loss. Two review articles reported their unpublished data , 8. Specwith SHL .CDH23 and SLC26A4 did not be counted in SHL, and they were conservatively categorized in NSHL. CDH23 is associated with Usher syndrome type ID  and nonsyndromic autosomal recessive deafness 12 (OMIM #601386). Patients with a truncated mutation  are mostly associated with USH1D, whereas those with missense mutations usually appear to be nonsyndromic [CDH23 were considered as NSHL in our study.We wish to stress that the clinical diagnosis of a specific condition is of great challenge, particularly in a gene associated with NSHL and SHL. To not exaggerate the proportion of SHL and NSHL mimics, we only counted patients whose genotype is strongly associated with syndromic phenotypes in public literature. Following this rule, patients with pathogenic variants in yndromic . Howeveryndromic . TherefoSLC26A4 is another good example to demonstrate the complexity. Mutations of SLC26A4 cause Pendred syndrome (OMIM #274600) and nonsyndromic autosomal recessive deafness 4 (OMIM #600791). Pendred syndrome is distinguished by the presence of thyroid abnormalities which are incompletely penetrant, adolescence onset, and partially influenced by nutritional iodine intake [SLC26A4 had normal thyroid by the date of follow-up, we conservatively considered them as NSHL in our study. However, regular monitoring of the thyroid function was still suggested, which may be abnormal during adolescence [e intake , 30. To lescence . Taken tKCNQ1 in P54. Individuals with JLNS can have significant cardiac events in early childhood [Another important issue is to understand the underlying mechanisms of NSHL mimics. One possible explanation is that clinical phenotypes of a given syndrome are not yet present or not easily evaluated by otolaryngologists at the time when NSHL was diagnosed. For example, children diagnosed with Usher syndrome may manifest retinitis pigmentosa many years after the onset of hearing impairment . Two chihildhood . JLNS isVariable penetrance might be another explanation for NSHL mimics. The same pathogenic variant associated with SHL may show incomplete penetrance and present differently between different people. Three patients  were genetically diagnosed with Waardenburg syndrome without additional clinical manifestations. However, the relatives harboring the same pathogenic variant displayed mild abnormal pigmentation of the hair. These variable manifestations within the same families suggest a variable penetrance of these variants and the phenotypic heterogeneity .GJB2, which inherited from his nonsyndromic hearing-impaired mother. Further exome sequencing eliminated other possible causes of nonsyndromic dominant deafness. Previous studies have demonstrated c.263C>T is associated with Keratitis-ichthyosis-deafness (KID) syndrome, and may cause infant early lethality [GJB2, p.Gly45Glu. The pathogenic effect of p.Gly45Glu can be confined by the presence of another heterozygous nonsense mutation p.Tyr136Ter, leading to healthy phenotype [Another case of the effect of variable penetrance is the diagnosis with P33. The proband had a heterozygous c.263C>T , in cis with an AR pathogenic variant , in henotype . Likewishenotype , and shoIn conclusion, syndromic hearing loss and nonsyndromic hearing loss mimics are common in pediatric patients who initially present with isolated hearing loss. The comprehensive genetic testing provides not only a high diagnostic yield but also valuable information for clinicians to uncover subclinical or pre-symptomatic phenotypes, which allows early diagnosis of SHL, and leads to precise genetic counseling and changes the medical management.Additional file 1: Figure S1. Pedigree with GJB6 deletion. Figure S2. Pedigree with STRC homozygous deletion.Additional file 2: Table S1. Phenotypic and genotypic characteristics of patients evaluated in this study.Additional file 3: Table S2. The genetic diagnosis results of 80 patients.


OTOF gene are a common cause of hereditary hearing loss and the main cause of auditory neuropathy spectrum disorder (ANSD). Although it is reported that most of the patients with OTOF mutations have stable, congenital or prelingual onset severe-to-profound hearing loss, some patients show atypical clinical phenotypes, and the genotype–phenotype correlation in patients with OTOF mutations is not yet fully understood. In this study, we aimed to reveal detailed clinical characteristics of OTOF-related hearing loss patients and the genotype–phenotype correlation. Detailed clinical information was available for 64 patients in our database who were diagnosed with OTOF-related hearing loss. As reported previously, most of the patients (90.6%) showed a “typical” phenotype; prelingual and severe-to-profound hearing loss. Forty-seven patients (73.4%) underwent cochlear implantation surgery and showed successful outcomes; approximately 85–90% of the patients showed a hearing level of 20–39 dB with cochlear implant and a Categories of Auditory Performance (CAP) scale level 6 or better. Although truncating mutations and p.Arg1939Gln were clearly related to severe phenotype, almost half of the patients with one or more non-truncating mutations showed mild-to-moderate hearing loss. Notably, patients with p.His513Arg, p.Ile1573Thr and p.Glu1910Lys showed “true” auditory neuropathy-like clinical characteristics. In this study, we have clarified genotype–phenotype correlation and efficacy of cochlear implantation for OTOF-related hearing loss patients in the biggest cohort studied to date. We believe that the clinical characteristics and genotype–phenotype correlation found in this study will support preoperative counseling and appropriate intervention for OTOF-related hearing loss patients.Mutations in the The online version contains supplementary material available at 10.1007/s00439-021-02351-7. OTOF is reported to be the causative gene of DFNB9 and one of the common causes of non-syndromic recessive sensorineural hearing loss. The prevalence of OTOF mutations has been reported to be 1.4–3.2% of non-syndromic hearing loss cases , which is a specific form of hearing loss with an abnormal auditory brainstem response (ABR) and the presence of otoacoustic emissions (OAEs) , and mutations in the OTOF gene cause dysfunction of synaptic exocytosis at the ribbon synapse   classified by a pure-tone average over 500, 1000, 2000 and 4000 Hz in the better hearing ears. For children who could not undergo PTA, we used an average over 500, 1000, 2000 and 4000 Hz in conditioned oriented reflex audiometry (COR). Severity of hearing loss was classified as follows; normal hearing, < 25 dB; mild hearing loss, 25–39 dB; moderate hearing loss, 40–69 dB; severe hearing loss, 70–89 dB; and profound hearing loss, greater than 90 dB. Written informed consent was obtained from all subjects  prior to enrollment in the project. This study was approved by the ethical committees of Shinshu University and each of the other participating institutions.A total of 12,137 patients were registered in our database between February 2012 and December 2020 from 96 otolaryngology departments from across Japan. Seventy-four patients had two or more mutations in the Amplicon libraries were prepared using an Ion AmpliSeq Custom Panel , in accordance with the manufacturer’s instructions, for 68 genes reported to cause non-syndromic hereditary HL. The detailed sample preparation protocol has been described elsewhere  were compound heterozygote with p.Arg1939Gln and another mutation. Eight patients (12.5%) had other non- p.Arg1939Gln mutations. Two patients excluded from the study were found to possess compound heterozygotes with p.[Pro309Leu];[Arg813Trp] and p.[Asp217Gly];[Ala1802Val];c.[3127-5G > A], respectively. The pathogenicity of the mutations found in the study were categorized in accordance with the ACMG criteria (Suppl. Table 1). Six mutations (c.2437C > T (p.Arg813Trp), c.3126 + 5G > A, c.3570 + 5G > A, c.5838G > A (p.Trp1946Ter), c.5728G > A (p.Glu1910Lys) and c.5500delG (p.Asp1834fs)) were novel.Detailed mutational information from 64 patients whose clinical information was available is shown in Table OTOF-related hearing loss show congenital severe-to-profound hearing loss (90.6%) . AABR and OAE was performed in 32 and 12 patients, respectively. All of the 12 patients examined by OAE improperly passed NBHS, while 3 out of 32 patients (9.4%) passed AABR screening  Fig. a, NBHS o6%) Fig. b. The re6%) Fig. c. Figure6%) Fig. a. Among In this study, the performance of cochlear implantation was evaluated using PTA and the Categories of Auditory Performance (CAP) scale for 36 patients who had used CI for over 2 years after the initial implantation. Cochlear implantations were performed unilaterally or bilaterally depending on the decisions made at each institution Table . HearingFigure OTOF-related hearing loss. One of the main aims was to identify genotype–phenotype correlations. As reported previously, most of the patients (90.6%) participating in this study showed a “typical” phenotype; congenital or prelingual onset and severe-to-profound hearing loss  and c.[4718 T > C];[4129_4138del]); the PTA of all patients showed moderate hearing loss while their ABR showed no response. p.His513Arg is located near the p.Ile515Thr and p.Gly541Ser mutations in the C2C domain, which are reported as mutations related to temperature-sensitive auditory neuropathy (TS-AN) and milder hearing loss in a non-febrile state  successfully improved the prognosis of hearing impairment in Otof−/− mice Below is the link to the electronic supplementary material.


Artificial intelligence (AI) has been applied in the field of retina. The purpose of this study was to analyze the study trends within AI in retina by reporting on publication trends, to identify journals, countries, authors, international collaborations, and keywords involved in AI in retina.A cross-sectional study. Bibliometric methods were used to evaluate global production and development trends in AI in retina since 2012 using Web of Science Core Collection.A total of 599 publications were retrieved ultimately. We found that AI in retina is a very attractive topic in scientific and medical community. No journal was found to specialize in AI in retina. The USA, China, and India were the three most productive countries. Authors from Austria, Singapore, and England also had worldwide academic influence. China has shown the greatest rapid increase in publication numbers. International collaboration could increase influence in this field. Keywords revealed that diabetic retinopathy, optical coherence tomography on multiple diseases, algorithm were three popular topics in the field. Most of top journals and top publication on AI in retina were mainly focused on engineering and computing, rather than medicine.These results helped clarify the current status and future trends in researches of AI in retina. This study may be useful for clinicians and scientists to have a general overview of this field, and better understand the main actors in this field . Researches are supposed to focus on more retinal diseases, multiple modal imaging, and performance of AI models in real-world clinical application. Collaboration among countries and institutions is common in current research of AI in retina. The application of artificial intelligence (AI) in retinal images has shown reliable performance as well as or better than human clinicians at some key medical care tasks, such as analysis of images, diagnosis, and prediction of prognosis . CurrentDue to these issues, a comprehensive review of the publications in AI in retina is urgently needed. Bibliometric analyses are helpful to address these problems by describing distribution patterns of publications, geographical distribution of research, latest evolution in the field of AI in retina. Therefore, bibliometrics is helpful in understanding of a specific field and in governing policymaking . BibliomWeb of Science (WOS) Core Collection is regarded as the most suitable database for bibliometric analysis. The search for papers to be included in the current study was carried on July 4, 2022, and all the included publications were published from January 1, 2012 to July 1, 2022. The search strategy was “ AND (TS = retina OR TS = vitreous OR TS = choroid)”. 622 literatures were identified. 77 publications were excluded except articles and review articles according to the document type, and 6 non-English publications were also excluded. 599 publications were included ultimately.All the data were extracted and downloaded from WOS databases, including metrics of publication numbers, countries and regions, authors, citations, and H-indexes. The classification of countries and regions were defined according to the default classification in Web of Science. The literatures from Hong Kong were part of the literatures from People’s Republic of China (China). We also investigated the relationship between global productivity of AI in retina and Human development index (HDI), which measures the level of human development based on knowledge, life expectancy, and income per capita indicators, rather than economic growth alone. Human development report 2020 was published by United Nations Development Programme . CountriThe descriptive indexes were extracted from WOS and calculated by SPSS. The co-occurrence networks of keywords, authors, and countries/regions were constructed by VOSviewer. The keywords were extracted from titles and abstracts. Frequencies over 20 was the criteria of the inclusion for analyses. Average appearing year was used to assess the novelty of keywords. For creating the wordcloud of keywords, Biblioshiny, an R tool, was used to generate wordcloud map of keywords. Frequencies over 20 was also the criteria of the inclusion for analyses. H-indexes were collected from WOS database, and can partially reflect the impact of researchers. Relative research interest (RRI) was defined as the number of publications in a specific field per year divided by all publications in all fields per year , followed by China  . More thWe analyzed the co-occurrence of 32 countries and regions ; the anaP value > 0.1).The top 10 countries/regions of high degree of collaboration were Singapore (95.2%), England (87.1%), Germany (85.7%), Saudi Arabia (84.4%), Switzerland (84.2%), Pakistan (79.2%), Australia (75.0%), Canada (68.0%), South Korea (67.4%), and USA (63.7%). Although China contributed a large number of publications, the degree of collaboration of China was only 39.6%. We further investigated the correlation among countries and regions, and found no significant correlation was detected between countries  and H-index (28)  from 201The most cited publication has been cited for a total of 481 times . We diviTo further explore the distribution of citation number in each year, we supplemented the heatmaps of each group of citation frequency . Every rWe examined the top institutions in this field and found that the University of College London , and the Moorfields Eye Hospital NHS Foundation Trust  in England published the most papers on AI in retina since 2012. University of California system , Johns Hopkins University , and Chinese Academy of Sciences  ranked third and fourth .IEEE Access, which published the most relevant publications (45). Translational Vision Science Technology and British Journal of Ophthalmology and published the second- and third-most with both 21 publications  of the papers on AI in retina were published in 27 journals, including ications .IEEE Transactions on Medical Imaging, a classic and authoritative medical imaging periodical, and is called Segmenting Retinal Blood Vessels With Deep Neural Networks. The corresponding author was Pawel Liskowski. Most publications of AI in retina were published in journals focusing on engineering and computing , optical coherence tomography (OCT) on multiple diseases-related cluster (in green), algorithm-related cluster (in blue) .We also color-coded the keywords by average time of appearance, and found that these keywords appeared in a short period . And theThe current study aims to conduct a bibliometric analysis related to AI in retina. We found that the increasing trends in AI in retina since 2012. USA has published the most publications (45 publications), the most citations  and the highest H-index (24). China shows the greatest potential on publications in this field. The authors who published most on AI in retina come from Austria, England, USA, Singapore, South Korea, and China. By analyzing keywords, we summarized previous hotspots and predicted future hotspots. We found that AI algorithm studies, diabetic retinopathy-related AI studies, OCT-related AI studies, and other application studies of AI on retina are the current hotspots in the field of AI in retina. The average years of appearance for keywords are concentrated over a relatively short period, which suggests that AI in retina is an attractive topic in science. These results suggest the rapid progress made in AI in retina research, which might guide the research directions of future studies.deep learning as topic words rather than other AI methods to search studies. Moreover, as this scientific research field is extremely dynamic . In the year when Google published its influential work of AI in detection of diabetic retinopathy , MicrosoWhen analyzing countries and authors, we found that the countries which published most papers were not consistent with the countries which had most influential authors. For example, USA (171 publications) and China (149 publications) contributed to the greatest number of publications, but only 1 author in the top 5 authors with greatest number of citations came from USA. On the contrary, authors from Austria and Singapore had the top worldwide influence according to the number of citations. On one hand, except benefiting from the bonus of number of researchers, the analysis of co-occurrence of countries and regions revealed that researchers in USA and China tend to collaborate with researchers from other countries . On the Besides the effort from medical researchers and AI engineers, a concerted effort from all stakeholders, including administrations, patients, and insurances, is needed. Sustainable models of AI applications in real world worth further investigation, taking the benefit of the patient, the health care provider, and the payer into consideration . EthicalWe analyzed the top journals which published most papers and top articles which were most cited, and found that most publications of AI in retina are technical researches related to technology, engineering, and computing , not related to clinical practice. Similar to most AI researches in the category of ophthalmology, researches of AI in retina mainly focus on technical methods (7 of top 10 publications) . The anaBased on the analysis of keywords in the current studies, most studies of AI in retina are correlated with diabetic retinopathy (200 relevant publications in the current study) , 6, follMoreover, multimodal imaging method rather than single modal imaging method is currently mainstream in real-world settings. However, most AI studies used single modal images of fundus photography or optical coherence tomography as training data, and the performance of trained models usually cannot overcome the challenges from numerous variabilities in reality, including field of view, image magnification, image quality, and race origin . Only liWe analyzed the keywords and abstracts of current studies of AI in retina, and found that most studies focused on imaging analysis (436 relevant publications), such as segmentation and detection of lesions, and diagnosis. Recently, Ting et al. found that using AI system as an assistive tool to screen for diabetic retinopathy is an economical method in real-world settings . The tecAlthough studies of AI in retina is broad, it mainly focuses on detecting structures and lesions and diagnosing diseases from fundus photography and optical coherence tomography images through deep learning algorithms currently, trying to achieve expert-level performance in well-prepared experiment environment. Enhancing the performance of detecting retinal diseases in real world in encouraged . FirstlyOur study has several limitations. The nature of selection bias existed in the methods, including in favor of English-language journal and only papers published on authoritative and influential journals which were listed in WOS Core Collection were included in our analysis. Although most high-quality papers are included by most databases, the results might be partially affected. Moreover, using different searching method in WOS Core Collection could lead to different results, and the method we used did not include publications that were assigned same keywords by other databases, which would not result in unrepeatable searching results. Therefore, the total number of included publications using forementioned methods might be less than some other methods.In summary, this study comprehensively analyzed most published researches on AI in retina, and presents a current view of mainstream studies on AI in retina. AI in retina is a very attractive topic in researches, and the relevant technique developed rapidly, although no journal specializes in AI in retina. International collaboration is important in conduction of influential researches. Researches are supposed to focus on more retinal diseases, multiple modal imaging, and performance of AI models in real world. This study may help clinicians, but also scientists understand the current trend of publications of AI in retina, know the main actors in the field, and predict and guide the future developments in this research field.The original contributions presented in this study are included in the article/JY: design, definition of intellectual content, data acquisition, data analysis, manuscript preparation, and manuscript editing. SW: design, definition of intellectual content, data acquisition, data analysis, funding, manuscript preparation, and manuscript editing. RD and WY: manuscript review. YC: concepts and manuscript review. All authors contributed to the article and approved the submitted version.


A model was created to predict the tribological behavior of SiC–ZrO2–Al hybrid composites using statistical data analysis and gene expression programming (GEP) based on artificial intelligence. For the purpose of examining the impact of zirconia concentration, sliding distance, and applied stress on the wear behavior of hybrid composites, a comprehensive factor design of experiments was used. The developed GEP model was sufficiently robust to achieve extremely high accuracy in the prediction of the determine coefficient (R2), the root mean square error (RMSE), and the root relative square error (RRSE). The maximum state of the RMSE was 0.4357 for the GEP-1 (w1) model and the lowest state was 0.7591 for the GEP-4 (w1) model, while the maximum state of the RRSE was 0.4357 for the GEP-1 (w1) model and the minimum state was 0.3115 for the GEP-3 model (w1).In order to improve product format quality and material flexibility, variety of application, and cost-effectiveness, SiC, ZrO Duemposites ,7,8,9,10mposites . Hybrid 2 is a readily available, reasonably priced metal oxide with exceptional thermal, mechanical, and tribological properties [2–Al hybrid composites made utilizing the PM process, according to the extensive examination of several research publications [Because SiC enhances the tribological and mechanical properties, it has recently been widely used as a ceramic reinforcement ,13,14. Aoperties . There aications ,19.2 reinforced Al–SiC hybrid nano-composites have been found. In the current study, the tribological behavior of hybrid SiC–ZrO2–Al composites made by powder metallurgy was examined. The effects of the ZrO2 concentration, the sliding distance, and the applied load on the wear behavior of the hybrid composite were examined using a full factorial design of experiments.In the recent past, various researchers have used the GEP to estimate the various mechanical characteristics of various types of concrete ,21. In r−1) in order to prevent oxidation of the aluminum matrix [Zirconia powders, silicon carbide powders, and aluminum powders all had typical particle sizes of 10 m and 30–50 nm, respectively. In the most recent investigation, these reinforcing particles were applied. The PM technique was used to create the composites. First, a quantity of elemental powder with a minimum count of 0.0001 g was weighed using a digital scale . The powders were fully combined in a centrifugal type ball mill  using 8 mm diameter stainless steel balls and a ball-to-powder weight ratio of 10:1 in order to accomplish homogenization and minimize particle agglomeration. The milling time and speed settings were 15 min and 100 revolutions per minute, respectively. Green compacts measuring 8 mm in diameter and 13 mm in height were created by compressing the powder combination at a pressure of 585 MPa in a uniaxial hydraulic pallet press . The die wall was manually greased with zinc stearate before each compaction procedure. The compacted samples were sintered in a tube furnace at 450–470 °C for 60 min while under an argon environment . A total of candidate GEP models with a respectable level of fitness are selected out of them.The parameters and a list of the function set utilized in the four GEP models are shown in 2). The errors and regression value are given by the following equation:One model is preferable to another according to a set of standards for assessing the model correctness. The root-mean-square-error (RMSE) and the root relative square error are two examples of these criteria (RRSE). To assess the consistency of the numerical values of the squared regression, a further parameter is utilized, in which the anticipated and experimental data may be calculated -based models (a). The R2 in the maximum state  was associated with the GEP-3 (w1) model, however, the R2 in the lowest state (shown in this figure for a training mode) was related to the GEP-1 (w1) model. The wear loss of hybrid composite SiC–ZrO2–Al was predicted by all six GEP models, although model GEP-3 (w1) is preferred over other models. The lowest RMSE was shown in An evolutionary run with more chromosomes would take longer since the number of settings is often governed by the complexity of the issue and the number of potential solutions. Selecting the ideal setup parameters  for the ideal GEP model is a crucial next step.Change the number of genes, the number of gene heads, and the linking function in all models to achieve this. The optimum GEP model structure, according to 2–Al hybrid composites are shown in equation:The GEP-3 (w4) formulae for predicting the wear loss of the SiC–ZrO2–Al hybrid composites, concentration of zirconia, sliding speed, sliding distance, and applied load as practical parameters of wear loss, a sensitivity analysis was employed for the determination of the most effective practical parameter on the microhardness of the nanocomposite coatings. Given that the best model for predicting microhardness is GEP-3 (w4), GEP-3 was employed for the sensitivity analysis (w4). In the GEP-3 (w4) models, the value for each practical parameter was set to zero while the values for the other parameters varied. By turning off the effect of the most affected parameter on the GEP-3 (w4) model, namely the RMSE as the threshold, it is reasonable to have a larger deviation from the performance of the models [By considering the SiC–ZrOe models .2–Al hybrid composites, as can be seen in 2 from 5, 10, and 20% while maintaining the other process parameters such as sliding distance and applied stress, unchanged. Additionally, The current density significantly affected the wear loss of the SiC–ZrO2–Al hybrid composites. The tribological behavior of the Al–SiC–ZrO2 hybrid composites was predicted using gene expression programming (GEP) and statistical data analysis for potential automotive engine-based applications. The influence of ZrO2 concentration, applied stress, the wear behavior of hybrid composites, and sliding distance were examined for potential engineering applications using a complete factorial design of experiments. The GEP-1 (w1) model had the highest possible RMSE of 0.4357, while the GEP-4 (w1) model had the lowest possible RMSE of 0.7591. The GEP-1 (w1) model had the highest possible RRSE of 0.4357, while the GEP-3 model had the lowest possible RRSE of 0.3115. (w1).Powder metallurgy was used to create the SiC–ZrO


Hearing aids are prescribed to alleviate loss of audibility. It has been reported that about 31% of hearing aid users reject their own hearing aid because of annoyance towards background noise. The reason for dissatisfaction can be located anywhere from the hearing aid microphone till the integrity of neurons along the auditory pathway.To measure spectra from the output of hearing aid at the ear canal level and frequency following response recorded at the auditory brainstem from individuals with hearing impairment.A total of sixty participants having moderate sensorineural hearing impairment with age range from 15 to 65 years were involved. Each participant was classified as either Good or Poor Hearing aid Performers based on acceptable noise level measure. Stimuli /da/ and /si/ were presented through loudspeaker at 65 dB SPL. At the ear canal, the spectra were measured in the unaided and aided conditions. At auditory brainstem, frequency following response were recorded to the same stimuli from the participants.F0 encoding; F0 and F1 energies were significantly higher in good hearing aid performers than in poor hearing aid performers. Though the hearing aid spectra were almost same between good hearing aid performers and poor hearing aid performers, subtle physiological variations exist at the auditory brainstem.Spectrum measured in each condition at ear canal was same in good hearing aid performers and poor hearing aid performers. At brainstem level, better The result of the present study suggests that neural encoding of speech sound at the brainstem level might be mediated distinctly in good hearing aid performers from that of poor hearing aid performers. Thus, it can be inferred that subtle physiological changes are evident at the auditory brainstem in a person who is willing to accept noise from those who are not willing to accept noise. Hearing aid is one of the common rehabilitative measures for individuals with permanent hearing impairment. In some cases of hearing losses, hearing aids can be used transitory. However, hearing aid users often complain of background noise resulting in rejection of hearing AID.Nabelek et al.Despite advancement in hearing aid technology, some individuals accept hearing aid and others may reject in spite of fact that they have a similar hearing loss in terms of degree, type, and configuration. The variability in satisfaction from rehabilitative device might probably be due to the processing parameters of hearing aid, and/or at interaction between output of hearing aid and its acoustic parameters relayed through different parts of auditory pathway.Over the decades, researchers have used probe tube microphone (PTM) system to measure the effect of hearing aid processing on acoustics of speech. The PTM measurement reflects the acoustic effect of the factors such as pinna, ear canal, head and torso.The frequency following responses (FFR) has been extensively studied to understand the physiological processing of speech at the auditory brainstem level. The FFR is a phase-locked response to periodic aspects of stimuli, including speech, up to 1000 HzFrom the existing literature, it can be inferred that spectral analyses of hearing aid output obtained using the PTM give information on hearing aid processing at the ear canal level. Further, stimulus from the ear canal is relayed to the auditory brainstem level and is measured using FFR. The FFR will help in inferring the neural encoding of the ongoing speech. These measures give insight into the way in which the speech is neurally encoded at the brainstem level, in individuals with sensorineural hearing impairment who are classified as good and poor hearing aid performers having comparable type, degree and configuration of hearing loss. Therefore, the present study intends to investigate hearing aid output at the ear canal to determine extent of alteration caused by the hearing aid on the spectral parameters. In addition, way in which amplified speech is represented at the brainstem level in good and poor hearing aid performers also is being investigated. The objectives formulated for the study were to compare: (1) spectral changes between GHP and PHP in unaided and aided conditions at the ear canal using the PTM; and (2) neural encoding of speech sounds at auditory brainstem level in GHP and PHP.A total of 60 participants who had bilateral moderate sensorineural hearing loss with a flat configuration were involved in the study. Flat configuration was operationally defined as the difference between least and highest air-conduction thresholds being less than 20 dB in the range from 0.25 to 8 kHz.F0) and the first two formant frequencies (F1 and F2) of the vowel component of /da/ and /si/ stimuli. The onset to steady state F0, F1 and F2 within the transition duration (37 ms) of /da/ stimulus, and frequency components within the transition duration (42 ms) of /i/ portion of /si/ stimulus were measured using Praat (version 5.1.29) software.Naturally produced consonant vowel (CV) tokens were utilized as target test stimuli. An adult male with normal voice was used to record the CV tokens. The duration of /da/ and /si/ stimuli was 94 ms and 301 ms, respectively. For /da/, the voice onset time was 18 ms, burst duration was 5 ms, transition duration was 37 ms, and vowel duration was 34 ms. For /si/, the fricative duration was 159.3 ms, transition duration was 47.1 ms and the vowel duration was 94.6 ms. Both the stimuli were converted from ‘.wav’ to ‘.avg’ format using wavtoavg m-file of Brainstem tool box. The ‘.avg’ format of both the stimuli were band pass filtered from 30 to 3000 Hz using Neuroscan (Scan 2-version 4.4) to know the functional relationship between the acoustic structure of speech and the brain stem response to speech. The ‘stimulus.avg’, waveforms and spectrograms of the two CV tokens are depicted in Further, Kannada passage developed by Sairam and ManjulaDigital Behind The Ear (BTE) hearing aid was used to record the output at the ear canal and at the auditory brainstem response from each participant. According to the technical specifications, frequency range of test hearing aid extended from 0.210 to 6.5 kHz. The peak full-on gain was 58 dB and high-frequency average full-on gain was 49 dB. The functioning of the hearing aid was ensured at the beginning of the data collection and repeated periodically during data collection.Each participant was classified into good and poor hearing aid performer using the behavioural ANL test. The test hearing aid with custom ear mould was fitted to each participant and its gain was optimized. To optimize the hearing aid gain, six Ling's syllables were presented at a calibrated level of 65 dB SPL through the audiometer in a sound field. The gain and the frequency response of the hearing aid were manipulated for the audibility of each of six Ling's syllables, through fine tuning option. To know the extent to which spectral feature are preserved by the hearing aid, the output of the hearing aid to each stimulus was recorded at the ear canal using the probe tube microphone measure. Further, the FFR at the brainstem level was recorded to each stimulus, in both unaided and aided conditions.Acceptable noise level (ANL) evaluates the reaction of the listener to background noise while listening to speech. For the measurement of ANL, the method given by Nabelek et al.The recorded Kannada passage was routed through the auxiliary input to the loudspeaker of the audiometer. The presentation level set at the level of SRT. Gradually, the level was adjusted in 5 dB-steps to establish the most comfortable level (MCL) and then in smaller steps size of +1 and −2 dB, until the MCL was established reliably. After the MCL was established, speech noise was introduced at 30 dB HL. The level of the speech noise was increased in 5 dB-steps initially, and then in 2 dB-steps, to a point at which the participant was willing to accept the noise without becoming tired or fatigued while listening to and following the words in the story. The maximum level at which he/she could accept or put up with the speech noise without becoming tired was considered as the background noise level (BNL). The level of the speech noise was adjusted until participant was able to ‘put-up-with’ the noise while following the story. The resultant level was the BNL. The ANL quantifies the acceptable level of background noise and is calculated as the difference between MCL (dB HL) and BNL (dB HL).Each participant was fitted with the digital BTE test hearing aid using a custom made soft shell mould. The hearing aid was programmed using NAL-NL1 prescriptive formula. The real ear measurement was carried out to match the gain of hearing aid with the target gain objectively. Further, the Ling's six speech sounds were presented at 65 dB SPL to optimize the hearing aid gain. Through fine tuning option, the gain and the frequency shaping of the hearing aid were optimized for the audibility of Ling's six sounds.The level of the each signal  was varied in the audiometer so that the intensity measured was 65 dB SPL in sound level meter. Larson Davis 824 sound level meter (SLM) was positioned at the test ear of the participant. The SLM was set at fast weighting function, and it was ensured that the stimuli /da/ and /si/ were presented at 65 dB SPL, based on peak amplitude level read on the SLM. After the calibration of stimulus was ensured, output spectrum at the ear canal was recorded using the probe tube microphone measurement, in both unaided and aided conditions. The probe tube microphone in the ear canal picks up the spectral energies at approximately half-octave bands from 0.25 kHz to 8 kHz for each speech stimulus. The levels as a function of frequency from 0.25 kHz to 8 kHz, in octaves, were noted down for each stimulus, in the unaided and aided conditions.Each participant was seated comfortably in a reclining chair with arm. The electrode sites were cleaned up with skin preparing gel. Disc type silver coated electrodes were placed using conduction gel at the test sites. The FFR was recorded using vertical montage. The non-inverting electrode (+) was placed on the vertex (Cz), the ground electrode was on upper fore head (Fpz) and the inverting electrode (−) was placed on nose. It was ensured that the electrode impedance was less than 5 kΩ for each of the electrodes and that the inter-electrode impedance was less than 2 kΩ.Prior to recording, calibration of stimuli was ensured using Larson Davis System 824 SLM. The SLM was positioned at reference point. It is the point where the test ear of the participant would be positioned at the time of testing. The SLM was set at fast weighting function for the measurement. It was ensured that both stimuli /da/ and /si/ were presented at 65 dB SPL, based on peak amplitude level read on the SLM.The loudspeaker of the Auditory Evoked Potential equipment was placed at 45° Azimuth from the participant test ear, located at the calibrated position of 12 inch distance. The height of loudspeaker was adjusted to the level of participant test ear. The participant was instructed to ignore the stimulus and to watch a movie that was muted and played through a battery operated laptop computer. He/she was also asked to minimize the eye and head movement.For recording the unaided and the aided FFR, the stimulus /da/ was presented through loud speaker at the presentation level of 65 dB SPL to the test ear. The PC-based evoked potential system, Neuroscan 4.4 (Stim 2-version 4.4), controlled the timing of stimulus presentation and delivered an external trigger to the evoked potential recording system, Neuroscan (Scan 2-version 4.4). To allow for a sufficient refractory period within the stimulus sweep, while minimizing the total recording time, an inter-stimulus interval (ISI) of 93 ms. from offset to onset of the next stimulus was included for recording FFR to /da/ stimulus. A similar procedure was repeated to record the unaided and aided FFR for /si/ stimulus. However, for recording unaided and aided FFR to /si/ stimulus, an ISI of 113 ms was used. The order of stimuli while testing on each participant was counter balanced. The FFR was recorded from 1500 sweeps each in condensation and rarefaction polarities, delivered in a homogenous train using the stimulus presentation software Neuroscan 4.4 (Stim 2-version 4.4).The FFR recording was initiated once a stable electroencephalogram (EEG) was obtained. The ongoing EEG was converted from analogue-to-digital with the rate of 20,000 Hz. The continuous EEG was online band-pass filtered from 30 to 3000 Hz with 12 dB/octave roll-off. This was stored to disc for offline analysis.F0, F0 energy and F1 energy obtained for each stimulus. The continuous EEG data were epoched over a window of 160 ms for /da/ stimulus (which included a 30 ms pre-stimulus period and a 130 ms post-stimulus time). The response for /si/ stimulus was epoched over a window of 360 ms (which included a pre-stimulus period of 30 ms and a post-stimulus period of 330 ms). The epoched waveforms were corrected for baseline. The responses were averaged and filtered off-line from 0.030 kHz  to 3 kHz . All artefacts exceeding ±35 μV were rejected while averaging the response for each averaged response, in rarefaction and condensation polarity. A minimum of 1450 artefact-free epochs was ensured. The averaged waveforms of rarefaction and condensation polarities were added. Further, the added waveforms were created by averaging two trials recorded for each stimulus, in unaided and aided conditions.The output of the hearing aid in the ear canal for each stimulus in the unaided and aided conditions were analyzed for spectra. Further, the FFR recorded was analyzed for F0 in the FFR i.e., from noted ‘V’ peak latency till transition duration. Whereas, the latency of ‘a1’ corresponding to CV boundaryF0 in the FFR. Further, F0 energy and F1 energy were determined, using ‘Brainstem Toolbox’ which utilizes the FFT technique  software was used to perform the statistical analyses. The results obtained are discussed with respect to each objective.p > 0.05) and homogeneity on Levene's test (F < 2). The spectral energy (0.5–8 kHz in octaves) for both stimuli obtained from both groups, in unaided and aided conditions, was subjected to MANOVA. The result revealed that there was no significant difference between groups in the spectral energy at each octave frequency, in both the unaided and the aided conditions, for /ɖa/ and /si/ stimuli. Thus, the data of spectral energy was combined between groups. Descriptive analysis was carried out separately in the unaided and aided conditions. For /ɖa/ stimulus  in the unaided and aided conditions, for both the stimuli was analyzed. It was performed to determine representation of energy across frequencies at the ear canal, in good and poor hearing aid performers. The data of spectral energy met the assumption of normal distribution on Kolmogorov–Smirnov normality test  and homogeneity on Levene's test (F < 2) was also performed. Hence, an independent samples t-test was conducted on each data of FFR between GHP (n = 34) and PHP (n = 24) groups. From the mean value of F0 of FFR  and /si/ stimulus .The 0 of FFR , it can F0 energy and the F1 energy of FFR was compared between GHP and PHP groups to each stimulus. From F0 energy and F1 energy of FFR to each stimulus were higher in GHP than in PHP. Further, to know if there was any significant difference between GHP and PHP in the mean the F0 energy and the F1 energy of FFR for each stimulus, independent samples t-test was performed. The result revealed a significant higher F0 energy in GHP than in PHP for /da/ stimulus  and /si/ stimulus . Further, a significant higher F1 energy was observed in GHP than PHP for /da/  and /si/ stimulus .In addition, the The aim of the study was to investigate the representation of amplified speech at the ear canal and at the auditory brainstem from the good and the poor hearing aid performers.In the unaided condition for /da/ and /si/ stimuli, energy measured at 2 kHz was relatively larger than at other frequencies (in octave). Further, there was a decline in energy after 4 kHz i.e., an approximately 10 dB per octave for /da/ stimulus and 12 dB per octave for /si/ stimulus . This paF0 representation in the FFR to each stimulus (/da/ and /si/) remained robust and similar to that of the unprocessed filtered raw stimulus. This indicated that preserved spectral content from hearing aid is relayed to the auditory brainstem level. For /da/ stimulus, the mean F0 of FFR was higher in GHP (133.46 Hz) than in PHP (128.84), such that the difference was found to be significantly different. This was true for F0 of FFR for /si/ stimulus between GHP (134.42 Hz) and PHP (130.84 Hz). Further, the F0 of the aided stimulus of /da/ was 134.95 Hz and that for /si/ was 144.74 Hz. The difference in F0 (in Hz), between encoding of F0 at brainstem level and F0 of aided test stimulus was 1 Hz in GHP and 6 Hz in PHP for /da/ stimulus. Similarly, the difference noted was 10 Hz in GHP and 14 Hz in PHP for /si/ stimulus.The FFR in both unaided and aided conditions were obtained from all the participants. In the unaided condition, the brainstem responses were absent, as the stimuli (/da/ and /si/) were presented at 65 dB SPL, which failed to reach audibility. In the aided condition, the F0 was 5 Hz for /da/ and 4 Hz for /si/ stimulus. Though this difference was significant in the encoding of F0 between GHP and PHP for both stimuli, this may not bring a change in speaker identity. This is because, according to IlesF0 will not bring about a change in speaker identity. The finding of the study is in accordance with the research report by HoriiF0 between the same two stimuli does not cause difference in speaker identity. Additionally, the intra-subject variability of F0 in a normal vocal effort ranged between ±9.6 Hz.F0 of FFR to /da/ and /si/ stimuli was neurally well represented in GHP than PHP, and that both the groups were able to recognize the identity of the speaker.The mean difference between the GHP and PHP in the encoding of F0 energy and the F1 energy of FFR to each stimulus were significantly higher in GHP than PHP. The higher energies of F0 and F1 in GHP might be due to stronger efferent fibres that inhibit other harmonics that do not correspond to fundamental frequency and formant frequencies. This is in accordance with the research reports by AshmoreF0 and F1. In addition, the efferent mechanism might be stronger such that the efferent fibres inhibit the other harmonics which do not correspond to the fundamental frequency and formant frequencies, thereby fine tuning the auditory input. The excitatory and inhibitory mechanisms of neurons of the underlying neural generator of the inferior colliculus in GHP fire more or less precisely to the corresponding F0 and F1 components of the stimulus. The inference of the present study supports the findings reported by Krishnan.F0 and the F1 of FFR. Along with an active afferent pathway, the afferent auditory nerve generates the electrical activity more precisely corresponding to the F0 and the F1 of the stimulus. This involves the release of neurotransmitter, thereby reducing the trans-membrane threshold and increase in neural firing. In poor hearing aid performers, though similar physiological activity was present, probably a lack of precision in neural activity due to less sensitive afferent and weak efferent auditory pathway, might have failed to provide higher energy at harmonics corresponding to F0 and F1 of each stimulus. Thus, it is can be inferred from the present study that subtle physiological variations might be present at the inferior colliculus of the auditory pathway in the poor hearing aid performers with reference to that in good hearing aid performers.Further, it was noted that the Though the hearing aid preserved inherent cues in speech syllables, an effect of annoyance towards noise alters the neural encoding at auditory brainstem level. It infers that acoustic cues transferred by hearing aid are successfully relied at auditory brainstem level but subtle physiological alterations were present at auditory brainstem in those individuals who are annoyed from those who are not by noise.The study presents an evidence to use objective approaches to validate the hearing aid output at ear canal and at auditory brainstem level. Utilization of the real ear measurement for analyzing the hearing aid output in the ear canal will help in knowing the representation of inherent speech cues. Studying the encoding of amplified speech in individuals with hearing impairment with their annoyance level demonstrates a critical role of stimulus contingent response in the assessment of hearing aid algorithms. It solves some of the practical problems faced by the audiologists regarding setting of amplification parameters in providing the maximum usable information. Findings of the present study help the audiologist in counselling a hearing aid user regarding extent of benefit derived with best hearing aid prescribed.The authors declare no conflicts of interest.


Multiple ear infections is causing hearing impairment among children all over the world and the health and social consequences track into early adolescence and later in life, if not treated. The monitoring of prevalence in a population is important to assess the need for interventions in a population.th grade from Sisimiut town and the nearby settlements participated in a clinical examination to have ear-examination and pure tone audiometry. Participants filled out a questionnaire at home with their parents before the clinical examination, and hearing impairment was collected as individual self-reports and as audiometric measurements.One hundred eighty  five children from 5 to 10n = 111) were girls. 247 (70%) of the 355 available otoscopies were clinically assessed as normal. Cohen’s Kappa coefficient was 0.31. Eighteen children (10%) were found to have hearing impairment. None of the children had hearing aids. Test performance for self-reports were that sensitivity was 56% and specificity was 87%. The predictive value of a positive test was 31%, and the predictive value of a negative test was 95%. 32 children (17%) reported hearing impairment to the extent that they were not able to keep up in school, of which half reported that it had lasted for more than one year. 7 of the 32 children reporting hearing impairment (22%) reported that the extent of their hearing impairment was affecting their classroom experience so they were not able to follow.A total of 185 children between 9 and 15 years of age  were included, 60% (Self-reported and clinically screening for hearing impairment are two different concepts. Even though the two concepts are statistically correlated, the correlation coefficients are low. The test performance indicated that self-reported data might be measuring hearing as an experience in a social environment and not directly comparable to pure tone audiometry which examines hearing in controlled testing conditions. Since both measure hearing impairment, they supplement each other in research on impaired hearing, and the choice of measure should relate to the purpose and method of the investigation. Multiple ear-infections especially in early childhood is the leading cause of hearing impairment worldwide, and for main reasons such as poorer coverage of specialist doctors, and several risk factors present, the public health problem is especially profound in many indigenous populations , 2. SeveThe indigenous population in Greenland is known as a high risk population with regards to middle-ear infections , and conA recent study examining 185 children age 9 to 15, and found that 29% had hearing impairment using PTA of 15 dB for either low or high frequencies or both as threshold, and 10% had hearing impairment when using 25 dB as threshold [The research area of hearing disabilities in the Arctic region has historically received a lot of attention from both ear-nose-and-throat (ENT) doctors and epidemiologists. Whereas ENT doctors has a focus on the pathophysiology of ear infection, treatment modalities and complications such as hearing loss, at the individual level, large epidemiological surveys aim to study public health impacts, social, psychological and emotional consequences of hearing impairment. Public health advocates and policy makers are interested in monitoring the prevalence of hearing loss in the population to support the individual for the benefit of the society, and to prevent public health at a broader level from worsening.Reports of 10–20% of children having hearing impairment means that many individuals are suffering from compromised communication skills and worsening of psychosocial and emotional development 3;4). At a societal level, reported consequences has been of poorer reading skills, behavioral problems and lower IQ can affect the healthy development of future generations . At a so–14.Pure tone audiometry is considered the gold standard for measuring hearing loss in population-based studies and screenings, but clinical measures vary according to definitions of threshold levels. Furthermore, audiometric outcomes can be limited by ambient noise, examiner competence and the developmental stage and behavior of the child.When using self-reported measures on hearing loss collected from questionnaire data, the validity of the questions are depended on the child’s motivation and interest in the questionnaire and the developmental stage and behavior of the child. A lack of self-report measures to use for children and adolescents – who may never have experienced normal hearing, and would therefore not be able to identify a change in hearing levels  – led to the development of items for a questionnaire-based survey among Greenlandic school-children . In 2017th grade from the two schools in Sisimiut and the nearby settlements were invited to participate, corresponding to 422 children in total. Data was collected in September 2020.This present cross-sectional study included school-aged children in Sisimiut in Greenland. All children from 5 to 10Participants filled out a questionnaire with their parents or guardians before ear-examination and audiometry, and parent or guardian provided informed consent before taking part in the clinical examinations.The questionnaire used was made from a base of the questions from the Greenlandic contribution to the international Health Behaviour in School-aged Children (HBSC) study. The questionnaire focused on the self-reported questions about hearing impairment, and also included questions on social factors relevant to hearing impairment and risk factors for the development of hearing related diseases. The questionnaire was available in Danish and Greenlandic.”Does poor hearing prevent you from keeping up in class?” with the response categories; Yes and no [You answered YES to poor hearing preventing you from keeping up in class:” and asking respondents to reply to the following question on extent of hearing impairment;”How much does poor hearing prevent you from keeping up in class?” with the response categories; A little, Quite a lot, and I can’t keep up at all., a question on the source of knowledge to the hearing impairment;”How do you know you have poor hearing? ” with five response categories; I noticed myself, Someone in my family told me, One of my teachers told me, One of my friends told me and A doctor/nurse/health visitor told me., and lastly a question on the duration of impaired hearing;”For how long have you suffered from poor hearing? with five response categories; Less than 3 months, More than 3 months but less than a year, 1–5 years, 5–10 year, and More than 10 years.As described by Schnohr et al. (2019) the item measuring self-reported hearing impairment was formulated;s and no . If the As described by Schnohr et al. , the facThe clinical examinations are described in our earlier work by Jensen et al. (2021). All examinations were conducted in a room in the schools, selected to reduce background noise. All respondents were examined for 15–30 min including a conversation about the questionnaire and an audiological examination. The audiological examination was conducted by a medical student (JSJ) who prior to data collection had received simulator based training and clinical teaching at the Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Rigshospitalet, Denmark. All examinations and conversations with respondents were followed by a Greenlandic research assistant who supported all work during the examinations, including connecting with school-children as respondents and before, during and after the inclusion into the study and the examination.with hearing impairment and without hearing impairment. The definition of hearing impairment was based on the PTA for the low frequencies  and for the high frequencies  thresholds were obtained at six frequencies: 250, 500, 1000, 2000, 4000, and 6000 Hertz (Hz). Thresholds were determined manually according to guidelines from the American Speech-Language-Hearing (ASHA) Association. Hearing impairment was categorized into Analyses was performed to assess the statistical association between self-reported (SR) and clinically measured (CM) hearing impairment.A simple 2 × 2 table initially described the association between the SR and CM, and false positives as well as false negatives were examined. Secondly Cohen’s Kappa coefficient was calculated to assess the agreement between the two categorical items. The underlying assumption of using the Kappa coefficient is that the two categorical items tested are measuring the same construct. Univariate logistic regression examined SR by CM to assess the statistical association. The test performance was examined by calculating sensitivity, specificity and positive and negative predictive values.A total of 185 (44%) of the invited children between 9 and 15 years of age (median: 11 years) participated whereof 111 (60%) were girls. Thirty-two of the 185 respondents answered ‘yes’ to the question whether they were not able to follow class because of poor hearing, corresponding to 17%. When studying Table Univariate logistic regression showed an odds ratio (OR) of 8.2 (95% CI 2.9–23.1) of SR when having been classified with hearing impairment by CM.Further examination was done by calculating the When basing conclusions on impaired hearing among school-children in Sisimiut, the prevalence varies from 17% according to self-reports, to 10% according to clinical examination from the threshold chosen in the present survey. Choice of threshold levels determines the variation between the two types of measurement, but there are large inconsistencies identified when associating the two measures.Sensitivity measures the test’s ability to classify the ones with hearing loss correctly, and the result of 56% shows poor ability to do so. Specificity measures the test’s ability to classify the students with normal hearing correctly, and this happened in 87% of the measures.The likelihood of actually having impaired hearing when reporting a positive test result by self-reports were only 31%, which shows that other factors are at play when children are responding to a question on their hearing. The likelihood of having normal hearing when reporting to have normal hearing in the questionnaire were as good as 95%. Given that the sensitivity and positive predictive values are relatively low indicate that the self-reported measures should be seen as an indication of problems, that could benefit from being examined further.Testing with Kappa coefficient was done to include a traditional measure of correlation, which underlines that the two measures are not closely related. Kappa coefficient is used to measure inter-rater reliability, and taking account the possibility of an agreement occurring by chance. Since the assumption of using Kappa is that the two measures are related to the same underlying construct seems to be rejected.With these findings, it becomes apparent, that researchers and clinicians should allow for a “grey zone” of classifications including the personal assessment of hearing impairment. This “grey zone” can be quantified by further analyses of the 30 out of 185 children that showed inconsistencies between the SR and CM, that were first evaluated for a second opinion, and initial findings were confirmed. Given the degree of variations, a valid conclusion is to consider the two measurements of hearing impairment as covering different areas, and one explanation is that one is a consequence of the other. This is supported by a strong statistical association of SR by CM, with an OR of 8.2.As described by Schnohr et al. (2019) the item measuring self-reported hearing impairment was associated to the school-situation, since the item was developed for school-aged children. In the event, that a respondent was asked why he/she did not keep up in school, and the suggestion was made that is was due to impaired hearing. Such a question can simply nudge the respondent to reply ‘yes’ even though the underlying cause – to not paying attention to school – is something else. Often children are asked to pay attention for other reasons than impaired hearing, and it would be relevant to adjust for those related causes. Age, gender, socioeconomic background and overall self-perceived health has been demonstrated to be associated with disagreement between self-reported hearing and audiometric outcome among older adults and may also affect the responses of children , 16. HowImportant factors that could lead to self-reported hearing difficulties in school but a normal pure-tone audiogram at examination are auditory neuropathy spectrum disorders, auditory processing disorders and any attention deficit disorder. Furthermore, the acoustic environment in the school is important. Background noise in the classroom, size of the room, number and position of students will affect their ability to hear the teacher’s voice.In Greenland it is only possible – as in this study – to use pure tone audiometry. Hearing is a highly complex neurological process that involve not only the outer and inner ear but also lower and higher cerebral functions that are interacting and are influenced by psychological processes and by the broader context in which the hearing takes place. The individual perception of what is heard therefore is a meta process.Considerations can be taken to what the measurement properties are for each of the two methods, and taking into account if the conclusions derived should stem from a representative result for the entire population – in which case an epidemiological survey of a large representative samples of the population is most appropriate – or whether researchers are looking for clinical conditions causing hearing impairment – in which case clinical examinations of high risk populations is most appropriate.Among older populations audiometric measures was found to explain less than 50% of the variance in hearing handicap and it was suggested that hearing handicap in the elderly will be measured more appropriate via a self-report format rather than as an inference from audiometric data .In spite of findings that the two measurements are not measuring the same construct, it is clear that they are closely related. Irrespectively where the threshold levels are set, it is fair to presume that students that are classified as normal hearing will have problems following school, and it becomes evident that both measures have each their relevance, depending on the purpose and method of any given investigation. Based on the present study, it is not appropriate to conclude whether the self-reported or the measured collected from clinical examinations generally have the highest reliability, as both measurements have advantages and uncertainties, that could benefit from being taken into account.The Arctic population is a high-risk population for chronic conditions related to impaired hearing, and a continued focus on the monitoring and examinations of both representative and high-risk populations serves as an important topic for future public health in Greenland. The authors of the present paper are continuing to develop questionnaires to improve the monitoring of impaired hearing in Greenland, and the potential of e.g. using mobile phones for testing to improve the reach of clinical staff is one of the areas worth examining further.The two measurements of hearing impairment compared in the present study seems to cover substantially different areas, since there are large inconsistencies identified when correlating the two measures. Since both measure hearing impairment, they supplement each other in ways not yet examined into details. Results of a clinical test of hearing is challenged if the test conditions are not optimal. An optimal test condition of quiet space and controlled environment never reflects to everyday situations where the level and quality of hearing is an assessment perceived by an individual from a given situation. A number of conditions are relevant to consider when measuring a person’s hearing, and in the case of children in a school-setting, the perception of hearing may vary according to communication skills, problems with concentration, school readiness or even behavioral problems often referred to as causes of “not paying attention” or not being able to listen. The reliability of the different measures of hearing is relevant to examine further in studies, where the controlled clinical examinations is performed in connection to interviews on situations, where the hearing in a social environment can also be assessed.