Interface of Medical Sieve, an algorithm by IBM for assisting in clinical decisions.<unk>  is used in the<unk>  of breast cancer, lung cancer, colon cancer, prostate cancer, bone metastases, coronary artery disease, congenital heart defect, pathological brain detection, fracture detection, Alzheimer's disease, and diabetic retinopathy. Breast cancer<unk>  is used in screening mammography (X-ray examination of the female breast). Screening mammography is used for the early detection of breast cancer.<unk>  systems are often utilized to help classify a tumor as malignant or benign.<unk>  is especially established in US and the Netherlands and is used in addition to human evaluation, usually by a radiologist. The first<unk>  system for mammography was developed in a research project at the University of Chicago. Today it is commercially offered by iCAD and Hologic. However, while achieving high sensitivities,<unk>  systems tend to have very low specificity and the benefits of using<unk>  remain uncertain. A 2008 systematic review on<unk><unk>  detection in screening mammography concluded that<unk>  does not have a significant effect on cancer detection rate, but does undesirably increase recall rate (i.e. the rate of false positives). However, it noted considerable heterogeneity in the impact on recall rate across studies. Recent advances in machine learning, deep-learning and artificial intelligence technology have enabled the development of<unk>  systems that are clinically proven to assist radiologists in addressing the challenges of reading mammographic images by improving cancer detection rates and reducing false positives and unnecessary patient recalls, while significantly decreasing reading times. Procedures to evaluate mammography based on magnetic resonance imaging exist too. Lung cancer (bronchial carcinoma) In the<unk>  of lung cancer,<unk>  tomography with special three-dimensional<unk>  systems are established and considered as appropriate second opinions. At this a volumetric dataset with up to 3,000 single images is prepared and analyzed. Round lesions (lung cancer, metastases and benign changes) from 1 mm are detectable. Today all well-known vendors of medical systems offer corresponding solutions. Early detection of lung cancer is valuable. However, the random detection of lung cancer in the early stage (stage 1) in the X-ray image is difficult. Round lesions that vary from 5–10 mm are easily overlooked. The routine application of<unk>  Chest Systems may help to detect small changes without initial suspicion. A number of researchers developed<unk>  systems for detection of lung nodules (round lesions less than 30 mm) in chest radiography and CT, and<unk>  systems for<unk>  (e.g., distinction between malignant and benign) of lung nodules in CT. Virtual dual-energy imaging improved the performance of<unk>  systems in chest radiography. Colon cancer<unk>  is available for detection of colorectal polyps in the colon in CT colonography. Polyps are small growths that arise from the inner lining of the colon.<unk>  detects the polyps by identifying their characteristic "bump-like" shape. To avoid excessive false positives,<unk>  ignores the normal colon wall, including the haustral folds. Cardiovascular disease State-of-the-art methods in cardiovascular computing, cardiovascular informatics, and mathematical and computational modeling can provide valuable tools in clinical decision-making.<unk>  systems with novel image-analysis-based markers as input can aid vascular physicians to decide with higher confidence on best suitable treatment for cardiovascular disease patients. Reliable early-detection and risk-stratification of carotid atherosclerosis is of outmost importance for predicting strokes in asymptomatic patients. To this end, various noninvasive and low-cost markers have been proposed, using ultrasound-image-based features. These combine echogenicity, texture, and motion characteristics to assist clinical decision towards improved prediction, assessment and management of cardiovascular risk.<unk>  is available for the automatic detection of significant (causing more than 50% stenosis) coronary artery disease in coronary CT angiography (CCTA) studies. Congenital heart defect Early detection of pathology can be the difference between life and death.<unk> e can be done by auscultation with a digital stethoscope and specialized software, also known as<unk><unk>  auscultation. Murmurs, irregular heart sounds, caused by blood flowing through a defective heart, can be detected with high sensitivity and specificity.<unk><unk>  auscultation is sensitive to external noise and bodily sounds and requires an almost silent environment to function accurately. Pathological brain detection (PBD) Chaplot et al. was the first to use Discrete Wavelet Transform (DWT) coefficients to detect pathological brains. Maitra and Chatterjee employed the Slantlet transform, which is an improved version of DWT. Their feature vector of each image is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions chosen according to a specific logic. In 2010, Wang and Wu presented a forward neural network (FNN) based method to classify a given MR brain image as normal or abnormal. The parameters of FNN were optimized via adaptive chaotic particle swarm optimization (ACPSO). Results over 160 images showed that the classification accuracy was 98.75%. In 2011, Wu and Wang proposed using DWT for feature extraction, PCA for feature reduction, and FNN with scaled chaotic artificial bee colony (SCABC) as classifier. In 2013, Saritha et al. were the first to apply wavelet entropy (WE) to detect pathological brains. Saritha also suggested to use spider-web plots. Later, Zhang et al. proved removing spider-web plots did not influence the performance. Genetic pattern search method was applied to identify abnormal brain from normal controls. Its classification accuracy was reported as 95.188%. Das et al. proposed to use Ripplet transform. Zhang et al. proposed to use particle swarm optimization (PSO). Kalbkhani et al. suggested to use GARCH model. In 2014, El-Dahshan et al. suggested to use pulse coupled neural network. In 2015, Zhou et al. suggested to apply naive Bayes classifier to detect pathological brains. Alzheimer's disease<unk> s can be used to identify subjects with Alzheimer's and mild cognitive impairment from normal elder controls. In 2014, Padma et al. used combined wavelet statistical texture features to segment and classify AD benign and malignant tumor slices. Zhang et al. found kernel support vector machine decision tree had 80% classification accuracy, with an average computation time of 0.022s for each image classification. In 2019, Signaevsky et al. have first reported a trained Fully Convolutional Network (FCN) for detection and quantification of neurofibrillary tangles (NFT) in Alzheimer's disease and an array of other tauopathies. The trained FCN achieved high precision and recall in naive digital whole slide image (WSI) semantic segmentation, correctly identifying NFT objects using a SegNet model trained for 200 epochs. The FCN reached near-practical efficiency with average processing time of 45 min per WSI per Graphic Processing Unit (GPU), enabling reliable and reproducible large-scale detection of NFTs. The measured performance on test data of eight naive WSI across various tauopathies resulted in the recall, precision, and an F1 score of 0.92, 0.72, and 0.81, respectively. Eigenbrain is a novel brain feature that can help to detect AD, based on Principal Component Analysis or Independent Component Analysis decomposition. Polynomial kernel SVM has been shown to achieve good accuracy. The polynomial KSVM performs better than linear SVM and RBF kernel SVM. Other approaches with decent results involve the use of texture analysis, morphological features, or high-order statistical features Nuclear medicine<unk> x is available for nuclear medicine images. Commercial<unk> x systems for the<unk>  of bone metastases in whole-body bone scans and coronary artery disease in myocardial perfusion images exist. With a high sensitivity and an acceptable false lesions detection rate,<unk><unk>  automatic lesion detection system is demonstrated as useful and will probably in the future be able to help nuclear medicine physicians to identify possible bone lesions. Diabetic retinopathy Diabetic retinopathy is a disease of the retina that is diagnosed predominantly by fundoscopic images. Diabetic patients in industrialised countries generally undergo regular screening for the condition. Imaging is used to recognize early signs of abnormal retinal blood vessels. Manual analysis of these images can be time-consuming and unreliable.<unk>  has been employed to enhance the accuracy, sensitivity, and specificity of automated detection method. The use of some<unk>  systems to replace human graders can be safe and cost effective. Image pre-processing, and feature extraction and classification are two main stages of these<unk>  algorithms. Pre-processing methods Image normalization is minimizing the variation across the entire image. Intensity variations in areas between periphery and central macular region of the eye have been reported to cause inaccuracy of vessel segmentation. Based on the 2014 review, this technique was the most frequently used and appeared in 11 out of 40 recently (since 2011) published primary research. Histogram Equalization Sample Image. Left: Normal gray-scale fundoscopic image. Right: Post-histogram equalization processing. Histogram equalization is useful in enhancing contrast within an image. This technique is used to increase local contrast. At the end of the processing, areas that were dark in the input image would be brightened, greatly enhancing the contrast among the features present in the area. On the other hand, brighter areas in the input image would remain bright or be reduced in brightness to equalize with the other areas in the image. Besides vessel segmentation, other features related to diabetic retinopathy can be further separated by using this pre-processing technique. Microaneurysm and hemorrhages are red lesions, whereas exudates are yellow
Computer-aided diagnosis
Biological terrain assessment
Independent medical examination
Ultrasound research interface
Radiological information system
Low vision assessment
CODY Assessment
Basic sciences examination