<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Huizhong Wang</style></author><author><style face="normal" font="default" size="100%">Liu, Zihao</style></author><author><style face="normal" font="default" size="100%">Haihao Pan</style></author><author><style face="normal" font="default" size="100%">Kecen Liu</style></author><author><style face="normal" font="default" size="100%">Yujie Wen</style></author><author><style face="normal" font="default" size="100%">Yao Qin</style></author><author><style face="normal" font="default" size="100%">Jingyang Dang</style></author><author><style face="normal" font="default" size="100%">Mingjia Li</style></author><author><style face="normal" font="default" size="100%">Ziyang Cui</style></author><author><style face="normal" font="default" size="100%">Tingting Jiang</style></author><author><style face="normal" font="default" size="100%">Yang Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A 3-Dimensional-Optimized Artificial Imaging Model for the Skin Tumor Burden Assessment of Mycosis Fungoides</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Investigative Dermatology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3-Dimensional</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">modified Severity Weighted Assessment Tool (mSWAT)</style></keyword><keyword><style  face="normal" font="default" size="100%">Mycosis fungoides</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0022202X2502144X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">146</style></volume><pages><style face="normal" font="default" size="100%">55-63.e7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Mycosis fungoides is characterized by widespread skin patches that may progress to plaques and tumors, necessitating precise tumor burden assessment for staging and treatment guidance. However, existing methods, including the widely accepted modified Severity Weighted Assessment Tool (mSWAT), present significant challenges in routine practice owing to their time-consuming nature and interobserver variability. This study developed an artificial intelligence model, mSWAT-Net, to estimate mSWAT scores using clinical images of patients with mycosis fungoides. Notably, the overlap area segmentation submodule of mSWAT-Net addressed double-counting errors in multiangle photos through training on 3904 annotated images generated from 61 three-dimensional human images. Across 2463 standardized full-body photographs from 134 imaging series, mSWAT-Net demonstrated performance comparable with that of experienced cutaneous lymphoma specialists, achieving intraclass correlation coefficients of 0.917 (internal validation) and 0.846 (temporal validation) for mSWAT score. Moreover, mSWAT-Net outperformed 3 junior dermatologists in image-based scoring (intraclass correlation coefficient = 0.917 vs 0.777) and demonstrated robust performance when compared with ground truth derived from 3-dimensional patient imaging (intraclass correlation coefficient = 0.812). Finally, mSWAT-Net was deployed as a free web application to support mycosis fungoides management in clinical settings. These findings highlight the potential of mSWAT-Net as an accurate, automated clinical tool for facilitating patient follow-up, treatment monitoring, and remote consultations.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>