Initial testing of machine learning-based imaging of pigmented skin lesions with reflectance confocal microscopy in a clinical setting

We present the initial findings of two ML algorithms developed to automate reflectance confocal microscopy (RCM) of skin. On a retrospective test set of 141 pigmented lesions collected at MSKCC between 2011 and 2020, our DEJ detection algorithm identified the DEJ with a median precision of 3 “slices”. The algorithm was less precise on melanomas and on facial lesions. On a retrospective test set of 302 RCM mosaics, the segmentation algorithm identified nonspecific patterns with a sensitivity of 0.75 and specificity of 0.79. Prospectively, on 31 benign pigmented lesions, the DEJ detection algorithm was performed with a median precision of 6.18µm.