Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists

Abstract

To the Editor: Currently, pathologic melanoma classification is based on the—inevitably somewhat subjective—integration of several histologic features.1 Thus, discordance between pathologists classifying the same lesions can be substantial, and objective assistance tools are needed. The classification of dermoscopic skin lesion images based on convolutional neural networks (CNNs) works well.2 On a histologic level, our pilot studies provided a proof regarding the principle of CNN-based melanoma recognition using tiny sections of hematoxylin-eosin–stained digitized slides. We compared the ability of CNNs with that of 18 international expert pathologists from eight different countries to discriminate melanomas and nevi in a less artificial setting using hematoxylin-eosin–stained whole-slide images. Ensembles of 3 individual CNNs were trained and tested using single hematoxylin-eosin–stained whole-slide images of 50 individual melanomas and 50 nevi labeled by a panel of 2 experienced dermatopathologists according to the standard practice to provide the “ground truth” (Supplementary Figs 1 and 2 available via Mendeley at https://data.mendeley.com/datasets/j87c9jshxy/1, Supplementary Table I available via Mendeley at https://data.mendeley.com/datasets/j87c9jshxy/1). The same 100 digitized slides were diagnosed using a web-based survey by 18 international dermatopathologists, each with at least 5 years of experience.