A deep learning model for histopathological diagnosis of actinic keratosis: a diagnostic case control accuracy study
Key Points
The study aims to evaluate the accuracy of deep learning models in diagnosing actinic keratosis using histopathological images.
Developed a deep learning model (DLM) for diagnosis.
Conducted a diagnostic case control study.
Analyzed routine dermatopathology samples for accuracy.
DLMs demonstrated reliable detection of actinic keratosis.
Improvements in diagnostic accuracy were noted with advanced technology.
Abstract
Our findings demonstrate the potential of DLMs to reliably detect AK in routine dermatopathology, suggesting their future impact as technology continues to advance.