Abstract
The incidence of melanoma is increasing worldwide, requiring early detection to improve survival rates. Although dermoscopy is the standard non-invasive tool for diagnosing melanoma, it relies on experience and skill. Advances in optical imaging technologies and artificial intelligence have the potential to improve diagnostic accuracy. Our objective was to compare the diagnostic accuracy of novel non-invasive optical imaging techniques for melanoma detection. A systematic literature search was conducted in three databases (Medline, Embase, and CENTRAL) on November 15, 2023. Inclusion criteria focused on studies comparing the accuracy of optical imaging methods against histopathology. Outcomes consisted of measures of diagnostic accuracy. Random-effects meta-analyses were performed for each method with 95% confidence intervals to summarize all relevant effect sizes. Of the 16,239 records, 141 articles met the inclusion criteria, of which 138 articles were eligible for the meta-analysis. Reflectance confocal microscopy (RCM) and dermoscopy combined with artificial intelligence (DSC + AI) had the highest sensitivity (0.93), with DSC + AI showing higher specificity (0.77 [0.70-0.83]) than RCM (0.749 [0.7475-0.7504]). Multispectral imaging combined with AI also showed high sensitivity (0.92 [0.82-0.97]) and relatively high specificity (0.80 [0.67-0.89]). Standalone dermoscopy exhibited balanced sensitivity (0.87 [0.84-0.90]) and specificity (0.82 [0.78-0.86]). In melanoma diagnosis, both RCM and DSC + AI can serve as second-step optical evaluation methods for suspicious lesions following initial screening with DSC. By maintaining a strong emphasis on multimodal imaging, healthcare providers could improve early detection and outcomes for patients with melanoma.