Artificial intelligence‐based PRO score assessment in actinic keratoses from LC‐OCT imaging using Convolutional Neural Networks

Abstract

SummaryBackground and ObjectivesThe histological PRO score (I–III) helps to assess the malignant potential of actinic keratoses (AK) by grading the dermal‐epidermal junction (DEJ) undulation. Line‐field confocal optical coherence tomography (LC‐OCT) provides non‐invasive real‐time PRO score quantification. From LC‐OCT imaging data, training of an artificial intelligence (AI), using Convolutional Neural Networks (CNNs) for automated PRO score quantification of AK in vivo may be achieved.Patients and MethodsCNNs were trained to segment LC‐OCT images of healthy skin and AK. PRO score models were developed in accordance with the histopathological gold standard and trained on a subset of 237 LC‐OCT AK images and tested on 76 images, comparing AI‐computed PRO score to the imaging experts’ visual consensus.ResultsSignificant agreement was found in 57/76 (75%) cases. AI‐automated grading correlated best with the visual score for PRO II (84.8%) vs. PRO III (69.2%) vs. PRO I (66.6%). Misinterpretation occurred in 25% of the cases mostly due to shadowing of the DEJ and disruptive features such as hair follicles.ConclusionsThe findings suggest that CNNs are helpful for automated PRO score quantification in LC‐OCT images. This may provide the clinician with a feasible tool for PRO score assessment in the follow‐up of AK.