Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, China.
Beijing Jingdong Shangke Information Technology Co., Ltd, Beijing, China.
Technol Health Care. 2022;30(S1):143-153. doi: 10.3233/THC-228014.
Acne vulgaris is one of the most prevalent skin conditions, which harms not only the patients' physiological conditions, but also their mental health. Early diagnosis and accurate continuous self-monitoring could help control and alleviate their discomfort.
We focus on the development and comparison of deep learning models for locating acne lesions on facial images, thus making estimations on the acne severity on faces via medical criterion.
Different from most existing literature on facial acne analysis, the considered models in this study are object detection models with convolutional neural network (CNN) as backbone and has better interpretability. Thus, they produce more credible results of acne detection and facial acne severity evaluation.
Experiments with real data validate the effectiveness of these models. The highest mean average precision (mAP) is 0.536 on an open source dataset. Corresponding error of acne lesion counting can be as low as 0.43 ± 6.65 on this dataset.
The presented models have been released to public via deployed as a freely accessible WeChat applet service, which provides continuous out-of-hospital self-monitoring to patients. This also aids the dermatologists to track the progress of this disease and to assess the effectiveness of treatment.
痤疮是最常见的皮肤疾病之一，不仅会损害患者的生理状况，还会影响其心理健康。早期诊断和准确的持续自我监测有助于控制和缓解不适。
我们专注于开发和比较用于在面部图像上定位痤疮病变的深度学习模型，从而通过医学标准对面部的痤疮严重程度进行估计。
与大多数现有的面部痤疮分析文献不同，本研究中考虑的模型是基于卷积神经网络（CNN）作为骨干的目标检测模型，具有更好的可解释性。因此，它们可以更可靠地检测痤疮和评估面部痤疮严重程度。
使用真实数据进行的实验验证了这些模型的有效性。在一个开源数据集上，最高的平均精度（mAP）为 0.536。在该数据集上，痤疮病变计数的对应误差可以低至 0.43±6.65。
所提出的模型已通过部署为一个免费的微信小程序服务向公众发布，为患者提供了院外持续的自我监测。这也有助于皮肤科医生跟踪疾病的进展，并评估治疗效果。
