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Yintao Fu on Muck Rack

Yintao Fu

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A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy

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By Gengmin Niu, Yong Guan, Yifan Zhang, Yongchun Song, Songfeng Li, Tao Liu, Sheng Huang, Jingru Chen, Xiaofeng Wang, Wencheng Zhang, Junjie Chen, Jing Huang, Kunyu Yang, Jianzhong Cao, Hongqin Yuan, Xiaofeng Pei, Dongmei Wu, Yang Nan, Yao Lu, Lujun Zhao, Zhiyong Yuan, Maobin Meng, Yeman Liu, Yintao Fu, Donghe Zhao, Shuanshuan Guo, Ziye Yan
| Nature Verified
Abstract Widespread clinical implementation of rapidly evolving auto-segmentation tools remains constrained by a scarcity of high-quality prospective evidence. Here we show the results of a prospective, multicenter, observational trial (NCT05787522) evaluating the clinical performance of a deep learning model (iCurveE) for artificial intelligence (AI)-assisted delineation of organs at risk (OARs) in thoracic and breast cancer radiotherapy.
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