Mei Li-Hong, Cao Meng-Ke, Li Jing, Ye Xuan-Guang, Liu Xiang-Dong, Yang Gao
Department of Dermatology, Jinshan Hospital of Fudan University, Shanghai, China.
Department of Dermatology, Shanghai Eighth People's Hospital, Shanghai, China.
Front Oncol. 2025 Apr 24;15:1507322. doi: 10.3389/fonc.2025.1507322. eCollection 2025.
This study aimed to evaluate the effectiveness of deep learning model in assisting dermatologists in classifying basal cell carcinoma (BCC) from seborrheic keratosis (SK). The goal was to assess whether AI-assisted diagnostics could improve accuracy, reduce misdiagnoses, and potentially enhance clinical outcomes.
This prospective study included 707 patients with histopathologically confirmed BCC or SK as an internal dataset (validation cohort), along with 5572 patients from the ISIC public dataset as an external dataset (split into training and test cohort). The images were preprocessed and augmented before being fed into a deep learning model based on the CLIP ViT-B/16 architecture. The model's performance was assessed using the area under the receiver operating characteristic (ROC) curves (AUC). Two dermatologists, one with 3 years of experience and another with 15 years of experience, reviewed the cases before and after receiving the deep learning model's predictions. Net reclassification index (NRI) and integrated discrimination improvement (IDI), was used to quantify the improvement in reclassification performance.
The model achieved an AUC of 0.76 in the training cohort and 0.79 in the test cohort for differentiating between BCC and SK. In the validation cohort, the model demonstrated an AUC of 0.71. Dermatologist 1's AUC improved from 0.75 to 0.82 with deep learning model assistance, while Dermatologist 2's AUC increased from 0.79 to 0.82. NRI and IDI analysis revealed statistically significant improvements, with Dermatologist 1 showing a 18% improvement and Dermatologist 2 showing a 11% improvement. Additionally, attention mechanisms like Grad-CAM provided insights into the model's decision-making process, enhancing the interpretability of its predictions.
The deep learning model demonstrated significant potential in aiding dermatologists in classifying BCC from SK.
本研究旨在评估深度学习模型在协助皮肤科医生区分基底细胞癌（BCC）和脂溢性角化病（SK）方面的有效性。目标是评估人工智能辅助诊断是否可以提高准确性、减少误诊，并潜在地改善临床结果。
这项前瞻性研究纳入了707例经组织病理学确诊为BCC或SK的患者作为内部数据集（验证队列），以及来自国际皮肤影像协作组（ISIC）公共数据集的5572例患者作为外部数据集（分为训练和测试队列）。图像在输入基于CLIP ViT-B/16架构的深度学习模型之前进行了预处理和增强。使用受试者操作特征（ROC）曲线下面积（AUC）评估模型的性能。两位皮肤科医生，一位有3年经验，另一位有15年经验，在接收深度学习模型的预测前后对病例进行了回顾。使用净重新分类指数（NRI）和综合判别改善（IDI）来量化重新分类性能的改善。
该模型在训练队列中区分BCC和SK的AUC为0.76，在测试队列中为0.79。在验证队列中，该模型的AUC为0.71。在深度学习模型的协助下，皮肤科医生1的AUC从0.75提高到0.82，而皮肤科医生2的AUC从0.79提高到0.82。NRI和IDI分析显示有统计学意义的改善，皮肤科医生1显示出18%的改善，皮肤科医生2显示出11%的改善。此外，像Grad-CAM这样的注意力机制提供了对模型决策过程的见解，增强了其预测的可解释性。
深度学习模型在协助皮肤科医生区分BCC和SK方面显示出巨大潜力。
