Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan.
Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 32001, Taiwan.
Comput Math Methods Med. 2022 Feb 10;2022:7960151. doi: 10.1155/2022/7960151. eCollection 2022.
During the evaluation of body surface area (BSA), precise measurement of psoriasis is crucial for assessing disease severity and modulating treatment strategies. Physicians usually evaluate patients subjectively through direct visual evaluation. However, judgment based on the naked eye is not reliable. This study is aimed at evaluating the use of machine learning methods, specifically U-net models, and developing an artificial neural network prediction model for automated psoriasis lesion segmentation and BSA measurement. The segmentation of psoriasis lesions using deep learning is adopted to measure the BSA of psoriasis so that the severity can be evaluated automatically in patients. An automated psoriasis lesion segmentation method based on the U-net architecture was used with a focus on high-resolution images and estimation of the BSA. The proposed method trained the model with the same patch size of 512 × 512 and predicted testing images with different patch sizes. We collected 255 high-resolution psoriasis images representing large anatomical sites, such as the trunk and extremities. The average residual of the ground truth image and the predicted image was approximately 0.033. The interclass correlation coefficient between the U-net and dermatologist's segmentations measured in the ratio of affected psoriasis over the body area in the test dataset was 0.966 (95% CI: 0.981-0.937), indicating strong agreement. Herein, the proposed U-net model achieved dermatologist-level performance in estimating the involved BSA for psoriasis.
在评估体表面积（BSA）时，准确测量银屑病对于评估疾病严重程度和调整治疗策略至关重要。医生通常通过直接目视评估来评估患者。然而，基于肉眼的判断并不可靠。本研究旨在评估机器学习方法（特别是 U-net 模型）的应用，并开发一种用于自动银屑病病变分割和 BSA 测量的人工神经网络预测模型。采用深度学习对银屑病病变进行分割，以测量银屑病的 BSA，从而可以自动评估患者的严重程度。采用基于 U-net 架构的自动银屑病病变分割方法，重点关注高分辨率图像和 BSA 的估计。所提出的方法使用相同的补丁大小 512×512 对模型进行训练，并对不同补丁大小的测试图像进行预测。我们收集了 255 张代表大解剖部位（如躯干和四肢）的高分辨率银屑病图像。地面实况图像和预测图像之间的平均残差约为 0.033。在测试数据集上，U-net 与皮肤科医生分割之间的类间相关系数以受影响的银屑病与身体面积的比例衡量为 0.966（95%CI：0.981-0.937），表明存在很强的一致性。在此，所提出的 U-net 模型在估计银屑病受累 BSA 方面达到了皮肤科医生的水平。
