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

Kentley Jonathan; Miguel Cordova; Nicholas R. Kurtansky; Manu Jain; Veronica Rotemberg; Kivanc Kose; Milind Rajadhyaksha

doi:10.1117/12.2609873

7 March 2022 Initial testing of machine learning-based imaging of pigmented skin lesions with reflectance confocal microscopy in a clinical setting

Kentley Jonathan, Miguel Cordova, Nicholas R. Kurtansky, Manu Jain, Veronica Rotemberg, Kivanc Kose, Milind Rajadhyaksha

Author Affiliations +

Proceedings Volume PC11934, Photonics in Dermatology and Plastic Surgery 2022; PC119340G (2022) https://doi.org/10.1117/12.2609873
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

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.

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Kentley Jonathan, Miguel Cordova, Nicholas R. Kurtansky, Manu Jain, Veronica Rotemberg, Kivanc Kose, and Milind Rajadhyaksha "Initial testing of machine learning-based imaging of pigmented skin lesions with reflectance confocal microscopy in a clinical setting", Proc. SPIE PC11934, Photonics in Dermatology and Plastic Surgery 2022, PC119340G (7 March 2022); https://doi.org/10.1117/12.2609873

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