<unk><unk>  detection <unk> e), also called<unk>  <unk> x), are systems that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, and ultrasound diagnostics yield a great deal of information that the radiologist or other medical professional has to analyze and evaluate comprehensively in a short time.<unk>  systems process digital images for typical appearances and to highlight conspicuous sections, such as possible diseases, in order to offer input to support a decision taken by the professional.<unk>  also has potential future applications in digital pathology with the advent of whole-slide imaging and machine learning algorithms. So far its application has been limited to quantifying immunostaining but is also being investigated for the standard H&E stain.<unk>  is an interdisciplinary technology combining elements of artificial intelligence and<unk>  vision with radiological and pathology image processing. A typical application is the detection of a tumor. For instance, some hospitals use<unk>  to support preventive medical check-ups in mammography<unk>  of breast cancer), the detection of polyps in the colon, and lung cancer.<unk><unk>  detection <unk> e) systems are usually confined to marking conspicuous structures and sections.<unk>  <unk> x) systems evaluate the conspicuous structures. For example, in mammography<unk>  highlights microcalcification clusters and hyperdense structures in the soft tissue. This allows the radiologist to draw conclusions about the condition of the pathology. Another application is<unk> q, which quantifies, e.g., the size of a tumor or the tumor's behavior in contrast medium uptake.<unk><unk>  simple triage (CAST) is another type of<unk> , which performs a fully automatic initial interpretation and triage of studies into some meaningful categories (e.g. negative and positive). CAST is particularly applicable in emergency diagnostic imaging, where a prompt<unk>  of critical, life-threatening condition is required. Although<unk>  has been used in clinical environments for over 40 years,<unk>  usually does not substitute the doctor or other professional, but rather plays a supporting role. The professional (generally a radiologist) is generally responsible for the final interpretation of a medical image. However, the goal of some<unk>  systems is to detect earliest signs of abnormality in patients that human professionals cannot, as in diabetic retinopathy, architectural distortion in mammograms, ground-glass nodules in thoracic CT, and non-polypoid (“flat”) lesions in CT colonography.
Computer-aided diagnosis
Biological terrain assessment
Independent medical examination
Ultrasound research interface
Radiological information system
Low vision assessment
CODY Assessment
Basic sciences examination