https://www.sciencedaily.com/releases/2021/02/210217151108.htm

Computer-aided diagnosis (CAD) systems have been developed in recent years to try to solve this problem by analyzing images of skin lesions and automatically identifying SPLs, but so far have failed to meaningfully impact melanoma diagnosis. These CAD algorithms are trained to evaluate each skin lesion individually for suspicious features, but dermatologists compare multiple lesions from an individual patient to determine whether they are cancerous -- a method commonly called the "ugly duckling" criteria. No CAD systems in dermatology, to date, have been designed to replicate this diagnosis process.

Now, that oversight has been corrected thanks to a new CAD system for skin lesions based on convolutional deep neural networks (CDNNs) developed by researchers at the Wyss Institute for Biologically Inspired Engineering at Harvard University and the Massachusetts Institute of Technology (MIT). The new system successfully distinguished SPLs from non-suspicious lesions in photos of patients' skin with ~90% accuracy, and for the first time established an "ugly duckling" metric capable of matching the consensus of three dermatologists 88% of the time.