Computer-aided diagnosis of reflectance confocal images to differentiate between lentigo maligna (LM) and atypical intraepidermal melanocytic proliferation (AIMP)

Abstract

AbstractLentigo maligna (LM), a form of melanoma in situ that predominantly affects sun-exposed areas such as the face, has an ill-defined clinical border and has a high rate of recurrence. Atypical Intraepidermal Melanocytic Proliferation (AIMP) is a term used to describe the melanocytic proliferation of an uncertain malignant potential. Clinically and histologically, AIMP can be difficult to distinguish from LM, and indeed AIMP may in some cases progress to LM. Reflectance Confocal Microscopy (RCM) is often used to investigate these lesions non-invasively, however, RCM is often not readily available nor is the associated expertise for RCM image interpretation. Here, we demonstrate machine learning architectures that can correctly classify lesions between LM and AIMP on stacks of RCM images. Overall, our methods showcase the potential for computer-aided diagnosis in dermatology, which in conjunction with the remote acquisition, can expand the range of diagnostic tools in the community.