Computer Assisted Diagnosis
|Description||This chapter provides a very nice up to date overview as well as the vision on computer assisted diagnosisare 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. CAD 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.|
|Owner||Allan Halpern → send e-mail|
|Status update||May 13, 2017|
|Status by||Ralph P. Braun|
The early diagnosisis the identification of the nature and cause of a certain phenomenon. Diagnosis is used in many different disciplines with variations in the use of logic, analytics, and experience to determine "cause and effect". In systems engineering and computer science, it is typically used to determine the causes of symptoms, mitigations, and solutions of melanomaThis glossary term has not yet been described. remains challenging. Meta-analysis has demonstrated that the use of dermoscopyDermoscopy is a non invasive diagnostic method. by trained users improves diagnostic accuracy for melanoma compared to naked-eye examination alone, mainly by increasing sensitivity. As non-physicians detect the majority of melanomas and population-based melanoma screening by clinicians is not currently recommended  outside of Germany, there is interest in the development of dermoscopy image analysis algorithms to aid laypersons or non-dermatology physicians in melanoma detection.
A recent landmark paper demonstrates the potential of ‘deep learning’ algorithms to aid in melanoma diagnosis. In their study, the researchers used deep convolutional neural networks (CNNs) which they trained in a large dataset of clinical imagesThis glossary term has not yet been described. and then compared against 21 dermatologists for the binary classificationis a general process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood. of keratinocyte carcinomas versus benignis any condition that is harmless in the long run seborrheic keratosesThis glossary term has not yet been described.; and malignantThis glossary term has not yet been described. melanomas versus benign neviThis glossary term has not yet been described., with the CNN achieving performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. While the potential for computer assisted diagnosis is great, the lack of a large public dataset of validated skin images has limited the ability to directly compare the diagnostic performance of automated image analysis algorithms across studies and against clinicians. The ISIC ArchiveISIC Archive is the name of the image repository of the International Skin Imaging Collaboration (ISIC). (link available on dermoscopediaDermoscopedia is the name of this website and is providing state of knowledge information concerning dermoscopy - a non invasive diagnostic method. menu bar) was created to address this need and has hosted consecutive international ‘Challenges’ that have engaged the broader computer science community.
There are many potential barriers to the implementation of automated diagnosis in clinical care (e.g., vetting of the accuracy of the algorithms, potential for overdiagnosis and false reassurance, acceptance by the medical community) but it is logical to expect that some form of computer assisted triage or diagnosis is forthcoming in the near future. There are already hundreds of ‘apps’ for mobile devices that are being marketed as educational or assistive devices for melanoma early detection. The medicolegal and social landscape around the adoption of these apps is rapidly changing.