Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms

• Published in: Journal of cancer research and clinical oncology Vol. 148; no. 9; pp. 2497 - 2505
• Main Authors: Dascalu, A., Walker, B. N., Oron, Y., David, E. O.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Cancer Research; COVID-19; COVID-19 - diagnostic imaging; Deep Learning; Dermoscopy - methods; Hematology; Humans; Internal Medicine; Malignancy; Medicine; Medicine & Public Health; Melanoma; Neural networks; Oncology; Original Article – Clinical Oncology; Original – Clinical Oncology; Skin cancer; Skin diseases; Skin lesions; Skin Neoplasms - diagnostic imaging; Skin Neoplasms - pathology; Smartphone; Smartphones; Telemedicine; Tumors; Visual discrimination learning; Deep learning; Sonification; Dermoscopy; Preventive medicine; Telemedicine; Non-melanoma skin cancer
• ISSN: 0171-5216; 1432-1335; 1432-1335
• DOI: 10.1007/s00432-021-03809-x

Purpose Non-melanoma skin cancer (NMSC) is the most frequent keratinocyte-origin skin tumor. It is confirmed that dermoscopy of NMSC confers a diagnostic advantage as compared to visual face-to-face assessment. COVID-19 restrictions diagnostics by telemedicine photos, which are analogous to visual inspection, displaced part of in-person visits. This study evaluated by a dual convolutional neural network (CNN) performance metrics in dermoscopic (DI) versus smartphone-captured images (SI) and tested if artificial intelligence narrows the proclaimed gap in diagnostic accuracy. Methods A CNN that receives a raw image and predicts malignancy, overlaid by a second independent CNN which processes a sonification (image-to-sound mapping) of the original image, were combined into a unified malignancy classifier. All images were histopathology-verified in a comparison between NMSC and benign skin lesions excised as suspected NMSCs. Study criteria outcomes were sensitivity and specificity for the unified output. Results Images acquired by DI ( n  = 132 NMSC, n  = 33 benign) were compared to SI ( n  = 170 NMSC, n  = 28 benign). DI and SI analysis metrics resulted in an area under the curve (AUC) of the receiver operator characteristic curve of 0.911 and 0.821, respectively. Accuracy was increased by DI (0.88; CI 81.9–92.4) as compared to SI (0.75; CI 68.1–80.6, p  < 0.005). Sensitivity of DI was higher than SI (95.3%, CI 90.4–98.3 vs 75.3%, CI 68.1–81.6, p  < 0.001), but not specificity ( p  = NS). Conclusion Telemedicine use of smartphone images might result in a substantial decrease in diagnostic performance as compared to dermoscopy, which needs to be considered by both healthcare providers and patients.