Introduction to computer assisted diagnosis
Computer assisted diagnosis, Diagnosis, Artificial intelligence, Neural networks Introduction to computer assisted diagnosis – cite! Introduction to computer assisted diagnosis (message) Introduction to computer assisted diagnosis – participate!
The early diagnosis of melanoma remains challenging. Meta-analysis has demonstrated that the use of dermoscopy 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 images and then compared against 21 dermatologists for the binary classification of keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi, 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 Archive (link available on dermoscopedia 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.
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