Use of convolutional neural networks in skin lesion analysis using real world image and non-image data - Wong, Samantha C.; Ratliff, William; Xia, Meng; Park, Christine; Sendak, Mark; Balu, Suresh; Henao, Ricardo; Carin, Lawrence; Kheterpal, Meenal K. (Frontiers in Medicine, 2022)
Background Understanding performance of convolutional neural networks (CNNs) for binary (benign vs malignant) lesion classification based on real world images is important for developing a meaningful clinical decision support (CDS) tool. Methods We developed a CNN based on real world smartphone images with histopathological ground truth and tested the utility of structured electronic health record (EHR) data on model performance. Model accuracy was compared against three board-certified dermatologists for clinical validity. Results At a classification threshold of 0.5, the sensitivity was 79% vs 77% vs 72%, and specificity was 64% vs 65% vs 57% for image-alone vs combined image and clinical data vs clinical data-alone models respectively. The PPV was 68% vs 69% vs 62%, AUC was 0.79 vs 0.79 vs 0.69, and AP was 0.78 vs 0.79 vs 0.64 for image-alone vs combined data vs clinical data-alone models. Older age, male sex, and number of prior dermatology visits were important positive predictors for malignancy in the clinical data-alone model. Conclusion Additional clinical data did not significantly improve CNN image model performance. Model accuracy for predicting malignant lesions was comparable to dermatologists (model: 71.31% vs 3 dermatologists: 77.87%, 69.88%, and 71.93%), validating clinical utility. Prospective validation of the model in primary care setting will enhance understanding of the model’s clinical utility.