Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios - Na Gao; Jiaping Wang; Zheng Zhao; Xiao Chu; Bin Lv; Gangwen Han; Yuan Ni; Guotong Xie (Scientific Reports)
Abstract 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.