AUTHOR=Wong Samantha C. , Ratliff William , Xia Meng , Park Christine , Sendak Mark , Balu Suresh , Henao Ricardo , Carin Lawrence , Kheterpal Meenal K. 

TITLE=Use of convolutional neural networks in skin lesion analysis using real world image and non-image data

JOURNAL=Frontiers in Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.946937

DOI=10.3389/fmed.2022.946937

ISSN=2296-858X

ABSTRACT=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.