Department of Dermatology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
College of Artificial Intelligence, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Photodermatol Photoimmunol Photomed. 2023 Sep;39(5):498-505. doi: 10.1111/phpp.12891. Epub 2023 Jun 12.
Identifying treatment responders after a single session of photo-based procedure for hyperpigmentary disorders may be difficult.
We aim to train a convolutional neural network (CNN) to test the hypothesis that there exist discernible features in pretreatment photographs for identifying favorable responses after photo-based treatments for facial hyperpigmentation and develop a clinically applicable algorithm to predict treatment outcome.
Two hundred and sixty-four sets of pretreatment photographs of subjects receiving photo-based treatment for esthetic enhancement were obtained using the VISIA® skin analysis system. Preprocessing was done by masking the facial features of the photographs. Each set of photographs consists of five types of images. Five independently trained CNNs based on the Resnet50 backbone were developed based on these images and the results of these CNNs were combined to obtain the final result.
The developed CNN algorithm has a prediction accuracy approaching 78.5% with area under the receiver operating characteristic curve being 0.839.
The treatment efficacy of photo-based therapies on facial skin pigmentation can be predicted based on pretreatment images.
单次光疗治疗色素障碍后，识别治疗应答者可能较为困难。
我们旨在训练卷积神经网络（CNN），以检验以下假设，即对于基于光疗的面部色素沉着治疗后的良好应答，在治疗前的照片中存在可识别的特征，并开发一种临床适用的算法来预测治疗效果。
使用 VISIA®皮肤分析系统获得 264 组接受基于光疗的美容增强治疗的患者的治疗前照片。通过对照片的面部特征进行遮罩处理来进行预处理。每组照片包含五种类型的图像。基于这些图像和这些 CNN 的结果，开发了五个独立训练的基于 Resnet50 骨干的 CNN，并将它们的结果进行组合以获得最终结果。
所开发的 CNN 算法的预测准确率接近 78.5%，接收器工作特征曲线下面积为 0.839。
可以基于治疗前图像预测基于光疗的疗法对面部皮肤色素沉着的治疗效果。
