Universität zu Köln

Maron, Roman C., Utikal, Jochen S., Hekler, Achim, Hauschild, Axel, Sattler, Elke, Sondermann, Wiebke, Haferkamp, Sebastian, Schilling, Bastian ORCID: 0000-0001-8859-4103, Heppt, Markus, V, Jansen, Philipp, Reinholz, Markus, Franklin, Cindy, Schmitt, Laurenz, Hartmann, Daniela, Krieghoff-Henning, Eva, Schmitt, Max, Weichenthal, Michael, von Kalle, Christof, Frohling, Stefan and Brinker, Titus J. (2020). Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study. J. Med. Internet Res., 22 (9). TORONTO: JMIR PUBLICATIONS, INC. ISSN 1438-8871

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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:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Maron, Roman C. UNSPECIFIED UNSPECIFIED UNSPECIFIED Utikal, Jochen S. UNSPECIFIED UNSPECIFIED UNSPECIFIED Hekler, Achim UNSPECIFIED UNSPECIFIED UNSPECIFIED Hauschild, Axel UNSPECIFIED UNSPECIFIED UNSPECIFIED Sattler, Elke UNSPECIFIED UNSPECIFIED UNSPECIFIED Sondermann, Wiebke UNSPECIFIED UNSPECIFIED UNSPECIFIED Haferkamp, Sebastian UNSPECIFIED UNSPECIFIED UNSPECIFIED Schilling, Bastian UNSPECIFIED orcid.org/0000-0001-8859-4103 UNSPECIFIED Heppt, Markus, V UNSPECIFIED UNSPECIFIED UNSPECIFIED Jansen, Philipp UNSPECIFIED UNSPECIFIED UNSPECIFIED Reinholz, Markus UNSPECIFIED UNSPECIFIED UNSPECIFIED Franklin, Cindy UNSPECIFIED UNSPECIFIED UNSPECIFIED Schmitt, Laurenz UNSPECIFIED UNSPECIFIED UNSPECIFIED Hartmann, Daniela UNSPECIFIED UNSPECIFIED UNSPECIFIED Krieghoff-Henning, Eva UNSPECIFIED UNSPECIFIED UNSPECIFIED Schmitt, Max UNSPECIFIED UNSPECIFIED UNSPECIFIED Weichenthal, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED von Kalle, Christof UNSPECIFIED UNSPECIFIED UNSPECIFIED Frohling, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Brinker, Titus J. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-319284
DOI:	10.2196/18091
Journal or Publication Title:	J. Med. Internet Res.
Volume:	22
Number:	9
Date:	2020
Publisher:	JMIR PUBLICATIONS, INC
Place of Publication:	TORONTO
ISSN:	1438-8871
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language SKIN-CANCER; DIAGNOSIS; DERMATOSCOPY; DECISION; IMPACT Multiple languages Health Care Sciences & Services; Medical Informatics Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/31928