Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks
By P. Tschandl, C. Rosendahl, B. N. Akay, Giuseppe Argenziano, A. Blum, R. Braun, H. Cabo, J. Gourhant, J. Kreusch, A. Lallas and 13 more. JAMA dermatology, 2019
303 citations recorded by the source corpus.
Importance Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose. Objective To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience. Design, Setting, and Participants A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy. Main Outcomes and Measures The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures. Results Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P < .001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P = .001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P = .18). Conclusions and Relevance Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.
Subfields: Artificial Intelligence, Epidemiology, Oncology
View Paper: https://www.semanticscholar.org/paper/f01b512314420d53fa4209d732aba1ed77977730
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Citations in the corpus
- Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda (2022, background): “Tschandl et al. (2019) build an image-based artificial intelligence for skin cancer diagnosis to address the effects of varied representations of clinical expertise and multiple clinical workflows.”