Man against machine reloaded

Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.

Highlights

A market-approved convolutional neural network (CNN) trained on dermoscopic images was tested against 96 dermatologists.

Test data included a broad range of skin lesions and was compiled from external sources not involved in CNN training.

Dermatologists indicated their management decisions after reviewing clinical, dermoscopic, and textual case information.

In this setting dermatologists performed on par with the CNN’s classifications based on dermoscopic images alone.

Background

Convolutional neural networks (CNNs) efficiently differentiate skin lesions by image analysis. Studies comparing a market-approved CNN in a broad range of diagnoses to dermatologists working under less artificial conditions are lacking.

Materials and methods

One hundred cases of pigmented/non-pigmented skin cancers and benign lesions were used for a two-level reader study in 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Additionally, dermoscopic images were classified by a CNN approved for the European market as a medical device (Moleanalyzer Pro, FotoFinder Systems, Bad Birnbach, Germany). Primary endpoints were the sensitivity and specificity of the CNN’s dichotomous classification in comparison with the dermatologists’ management decisions. Secondary endpoints included the dermatologists’ diagnostic decisions, their performance according to their level of experience, and the CNN’s area under the curve (AUC) of receiver operating characteristics (ROC).

 

Results

The CNN revealed a sensitivity, specificity, and ROC AUC with corresponding 95% confidence intervals (CI) of 95.0% (95% CI 83.5% to 98.6%), 76.7% (95% CI 64.6% to 85.6%), and 0.918 (95% CI 0.866–0.970), respectively. In level I, the dermatologists’ management decisions showed a mean sensitivity and specificity of 89.0% (95% CI 87.4% to 90.6%) and 80.7% (95% CI 78.8% to 82.6%). With level II information, the sensitivity significantly improved to 94.1% (95% CI 93.1% to 95.1%; P < 0.001), while the specificity remained unchanged at 80.4% (95% CI 78.4% to 82.4%; P = 0.97). When fixing the CNN’s specificity at the mean specificity of the dermatologists’ management decision in level II (80.4%), the CNN’s sensitivity was almost equal to that of human raters, at 95% (95% CI 83.5% to 98.6%) versus 94.1% (95% CI 93.1% to 95.1%); P = 0.1. In contrast, dermatologists were outperformed by the CNN in their level I management decisions and level I and II diagnostic decisions. More experienced dermatologists frequently surpassed the CNN’s performance.

Conclusions

Under less artificial conditions and in a broader spectrum of diagnoses, the CNN and most dermatologists performed on the same level. Dermatologists are trained to integrate information from a range of sources rendering comparative studies that are solely based on one single case image inadequate.

  1. Department of Dermatology, University of Heidelberg, Heidelberg, Germany
  2. Department of Dermatology, Allergology and Environmental Medicine II, Munich, Germany
  3. Department of Dermatology and Venerology, Medical University of Graz, Graz, Austria
  4. First Department of Dermatology, Aristotle University, Thessaloniki, Greece
  5. Department of Dermatology, University of Rostock, Rostock, Germany
  6. Department of Dermatology, University of Göttingen, Göttingen, Germany
  7. Department of Dermatology and Allergology, Klinikum Bremen-Mitte, Bremen, Germany
  8. Office Based Clinic of Dermatology, Konstanz, Germany
  9. Faculty of Computer Science and Mathematics, University of Passau, Passau, Germany
  10. Department of Dermatology, Lyons Cancer Research Center, Lyon 1 University, Lyon, France
  11. Department of Dermatology, Université Catholique de Louvain, St Luc University Hospital, Brussels, Belgium
  12. Department of Dermatology, Medical University of Vienna, Vienna, Austria
  13. Department of Genetic Epidemiology, University of Goettingen, Goettingen, Germany
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