Enhancing dermatological diagnosis for differentiating actinic from seborrheic keratosis using deep learning model.

ABSTRACT:

Background

Differentiating Actinic keratosis (AK) from Seborrheic keratosis (SK) can be challenging for dermatologists due to their visual similarities. This multi-center prospective study aims to investigate the efficacy of deep learning (DL) model in assisting dermatologists in accurately classifying AK from SK lesions.

Methods

A contrastive language-image pre-training (CLIP) model with ViT-B/16 architecture was trained on an dataset of 2,307 patients and validated in three separate datasets of 386 (from Center A), 196 patients (from Center B and C) and 215 patients (from DermNet). Two dermatologists classified the lesions separately. Then they were showed the model's predictions and were requested to reclassify the results if needed. Area under the receiver operating char

SUBMITTER: Ren YY 

PROVIDER: S-EPMC12528019 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature