Robustness of a convolutional neural network trained on dermoscopic images and challenged with close‐up images

Summary Background and Objectives Deep learning‐convolutional neural networks (DL‐CNNs) have demonstrated high diagnostic accuracy within the domain of dermoscopy. However, many clinical settings lack dermoscopic devices, requiring reliance on close‐up images. This study evaluates the robustness of a DL‐CNN trained on dermoscopic images when challenged with close‐up images. Methods In this cross‐sectional study, the DL‐CNN Moleanalyzer pro, trained on 129,487 dermoscopic images, was tested on 350 skin lesions, each imaged both clinically and dermoscopically. Histopathology (89.4%) or expert consensus with a two‐year follow‐up (10.6%) served as the reference standard. Primary outcomes included sensitivity, specificity, and the receiver operating characteristic‐area under the curve (ROC‐AUC). Results For dermoscopic images, the DL‐CNN achieved a sensitivity of 88.2% (95% CI: 82.9%–92.0%), specificity of 69.0% (61.4%–75.8%), and a ROC‐AUC of 0.866 (0.860–0.873). For close‐up images of the same lesions, sensitivity decreased to 60.5% (53.5%–67.1%, p < 0.001), while specificity increased to 79.4% (72.3%–85.0%, p = 0.027). The ROC‐AUC for close‐up images was 0.780 (0.772–0.790, p = 0.003). Conclusions Our findings highlight the diagnostic limitations of applying DL‐CNNs trained on dermoscopic images to close‐up images, with reduced sensitivity but increased specificity. The results emphasize the need to improve cross‐domain adaptability. Fine‐tuning of DL‐CNNs on clinical images may enhance real‐world diagnostic accuracy.