Technological advances for the detection of melanoma

• Published in: Journal of the American Academy of Dermatology 2020-10, Vol.83 (4), p.983-992
• Main Authors: Fried, Lauren, Tan, Andrea, Bajaj, Shirin, Liebman, Tracey N., Polsky, David, Stein, Jennifer A.
• Format: Article
• Language: English
• Subjects: artificial intelligence; confocal microscopy; dermoscopy; electrical impedance spectroscopy; machine learning; melanoma; sequential digital dermoscopic imaging; total body photography
• ISSN: 0190-9622; 1097-6787
• DOI: 10.1016/j.jaad.2020.03.121

Managing the balance between accurately identifying early stage melanomas while avoiding obtaining biopsy specimens of benign lesions (ie, overbiopsy) is the major challenge of melanoma detection. Decision making can be especially difficult in patients with extensive atypical nevi. Recognizing that the primary screening modality for melanoma is subjective examination, studies have shown a tendency toward overbiopsy. Even low-risk routine surgical procedures are associated with morbidity, mounting health care costs, and patient anxiety. Recent advancements in noninvasive diagnostic modalities have helped improve diagnostic accuracy, especially when managing melanocytic lesions of uncertain diagnosis. Breakthroughs in artificial intelligence have also shown exciting potential in changing the landscape of melanoma detection. In the first article in this continuing medical education series, we review novel diagnostic technologies, such as automated 2- and 3-dimensional total body imaging with sequential digital dermoscopic imaging, reflectance confocal microscopy, and electrical impedance spectroscopy, and we explore the logistics and implications of potentially integrating artificial intelligence into existing melanoma management paradigms.