Maron, Roman C. and Utikal, Jochen S. and Hekler, Achim and Hauschild, Axel and Sattler, Elke and Sondermann, Wiebke and Haferkamp, Sebastian and Schilling, Bastian and Heppt, Markus and Jansen, Philipp and Reinholz, Markus and Franklin, Cindy and Schmitt, Laurenz and Hartmann, Daniela and Krieghoff-Henning, Eva and Schmitt, Max and Weichenthal, Michael and von Kalle, Christof and Frohling, Stefan and Brinker, Titus J. (2020) Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study. JOURNAL OF MEDICAL INTERNET RESEARCH, 22 (9): e18091. ISSN 1438-8871,
Full text not available from this repository. (Request a copy)Abstract
Background: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist's diagnoses. Objective: The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image-based discrimination between melanoma and nevus. Methods: Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. Results: While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists' average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. Conclusions: The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image-based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SKIN-CANCER; DIAGNOSIS; DERMATOSCOPY; DECISION; IMPACT; artificial intelligence; machine learning; deep learning; neural network; dermatology; diagnosis; nevi; melanoma; skin neoplasm |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Dermatologie und Venerologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 11 Mar 2021 12:44 |
| Last Modified: | 11 Mar 2021 12:44 |
| URI: | https://pred.uni-regensburg.de/id/eprint/43808 |
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