Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition.
BACKGROUND
Studies systematically unravelling possible causes for false diagnoses of deep learning convolutional neural networks (CNNs) are scarce, yet needed before broader application.


OBJECTIVES
The objective of the study was to investigate whether scale bars in dermoscopic images are associated with the diagnostic accuracy of a market-approved CNN.


METHODS
This cross-sectional analysis applied a CNN trained with more than 150,000 images (Moleanalyzer-pro®, FotoFinder Systems Inc., Bad Birnbach, Germany) to investigate seven dermoscopic image sets depicting the same 130 melanocytic lesions (107 nevi, 23 melanomas) without or with digitally superimposed scale bars of different manufacturers. Sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for the CNN's binary classification of images with or without superimposed scale bars were assessed.


RESULTS
Six dermoscopic image sets with different scale bars and one control set without scale bars (overall 910 images) were submitted to CNN analysis. In images without scale bars, the CNN attained a sensitivity [95% confidence interval] of 87.0% [67.9%-95.5%] and a specificity of 87.9% [80.3%-92.8%]. ROC AUC was 0.953 [0.914-0.992]. Scale bars were not associated with significant changes in sensitivity (range 87%-95.7%, all p ≥ 1.0). However, four scale bars induced a decrease of the CNN's specificity (range 0%-43.9%, all p < 0.001). Moreover, ROC AUC was significantly reduced by two scale bars (range 0.520-0.848, both p ≤ 0.042).


CONCLUSIONS
Superimposed scale bars in dermoscopic images may impair the CNN's diagnostic accuracy, mostly by increasing the rate of the false-positive diagnoses. We recommend avoiding scale bars in images intended for CNN analysis unless specific measures counteracting effects are implemented.


CLINICAL TRIAL NUMBER
This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; URL: https://www.drks.de/drks_web/).
['Winkler|Julia K|JK|', 'Sies|Katharina|K|', 'Fink|Christine|C|', 'Toberer|Ferdinand|F|', 'Enk|Alexander|A|', 'Abassi|Mohamed S|MS|', 'Fuchs|Tobias|T|', 'Haenssle|Holger A|HA|']
D016477:Artifacts D003430:Cross-Sectional Studies D000077321:Deep Learning D046169:Dermoscopy D003936:Diagnosis, Computer-Assisted D006801:Humans D007090:Image Interpretation, Computer-Assisted D008545:Melanoma D009506:Nevus D011237:Predictive Value of Tests D015203:Reproducibility of Results D012189:Retrospective Studies D012878:Skin Neoplasms
Scale bar Deep learning Convolutional neural network Melanoma Dermoscopy Nevus