Gao Na, Wang Jiaping, Zhao Zheng, Chu Xiao, Lv Bin, Han Gangwen, Ni Yuan, Xie Guotong
Department of Dermatology, Peking University International Hospital, Beijing, China.
Ping An Technology, Shanghai, China.
Sci Rep. 2025 Jan 7;15(1):1119. doi: 10.1038/s41598-024-84670-z.
Accurate acne severity grading is crucial for effective clinical treatment and timely follow-up management. Although some artificial intelligence methods have been developed to automate the process of acne severity grading, the diversity of acne image capture sources and the various application scenarios can affect their performance. Therefore, it's necessary to design special methods and evaluate them systematically before introducing them into clinical practice. To develop and evaluate a deep learning-based algorithm that could accurately accomplish acne lesion detection and severity grading simultaneously in different healthcare scenarios. We collected 2,157 facial images from two public and three self-built datasets for model development and evaluation. An algorithm called AcneDGNet was constructed with a feature extraction module, a lesion detection module and a severity grading module. Its performance was evaluated in both online and offline healthcare scenarios. Experimental results on the largest and most diverse evaluation datasets revealed that the overall performance for acne severity grading achieved accuracies of 89.5% in online scenarios and 89.8% in offline scenarios. For follow-up visits in online scenarios, the accuracy for detecting the changing trends reached 87.8%, with a total counting error of 1.91 ± 3.28 for all acne lesions. Additionally, the prospective evaluation demonstrated that AcneDGNet was not only much more accurate for acne grading than junior dermatologists but also comparable to the accuracy of senior dermatologists. These findings indicated that AcneDGNet can effectively assist dermatologists and patients in the diagnosis and management of acne, both in online and offline healthcare scenarios.
准确的痤疮严重程度分级对于有效的临床治疗和及时的随访管理至关重要。尽管已经开发了一些人工智能方法来实现痤疮严重程度分级过程的自动化，但痤疮图像采集来源的多样性和各种应用场景会影响其性能。因此，在将其引入临床实践之前，有必要设计特殊的方法并对其进行系统评估。为了开发和评估一种基于深度学习的算法，该算法能够在不同的医疗场景中同时准确地完成痤疮病变检测和严重程度分级。我们从两个公共数据集和三个自建数据集中收集了2157张面部图像用于模型开发和评估。构建了一种名为AcneDGNet的算法，它具有一个特征提取模块、一个病变检测模块和一个严重程度分级模块。在在线和离线医疗场景中对其性能进行了评估。在最大且最多样化的评估数据集上的实验结果表明，痤疮严重程度分级的整体性能在在线场景中达到了89.5%的准确率，在离线场景中达到了89.8%的准确率。对于在线场景中的随访，检测变化趋势的准确率达到87.8%，所有痤疮病变的总计数误差为1.91±3.28。此外，前瞻性评估表明，AcneDGNet在痤疮分级方面不仅比初级皮肤科医生准确得多，而且与高级皮肤科医生的准确率相当。这些发现表明，AcneDGNet可以在在线和离线医疗场景中有效地协助皮肤科医生和患者进行痤疮的诊断和管理。
