The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study

Abstract

Actinic Keratosis (AK) is a frequent dermatological diagnosis which contributes to a large proportion of routine dermatopathology. A current development in histopathology is in the digitization of specimens by creating whole slide images (WSI) with slide scanners. Deep Learning Models (DLM) have been introduced to radiology or pathology for image recognition but dermatopathology lacks available solutions. Building on previous work about skin pathologies, this paper proposes a DLM following the U-Net architecture to detect AK in histopathological samples. In total, 297 histopathological slides (269 with AK and 28 without AK) have been retrospectively selected. They were randomly assigned to training, validation, and testing groups. Performance was evaluated by conducting a Case Control Accuracy Study on three levels of granularity. The DLM model achieved an overall accuracy of 99.13% on the WSI level, 99.02% on the patch level and an intersection over union (IoU) of 83.88%. The proposed DLM reliably recognizes AK in histopathological images, supporting the implementation of DLMs in dermatopathology practice. Given existing technical capabilities and advancements, DLMs could have a significant influence on dermatopathology routine in the future.