Development and evaluation of USCnet: an AI-based model for preoperative prediction of infectious and non-infectious urolithiasis.

Journal: World journal of urology
PMID: 40014115

Abstract

BACKGROUND: Urolithiasis, a prevalent condition characterized by a high rate of incidence and recurrence, necessitates accurate preoperative diagnostic methods to determine stone composition for effective clinical management. Current diagnostic practices, reliant on postoperative specimen analysis, often fail to facilitate timely and precise therapeutic decisions, leading to suboptimal clinical outcomes. This study introduces an artificial intelligence model developed to predict infectious and non-infectious urolithiasis preoperatively using clinical data and CT imaging.

Authors

  • Jiexin Pan
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China.
  • Haodong Chen
    Department of Urology, The Second Affiliated Hospital of The Chinese University of HongKong/Longgang District People's Hospital of Shenzhen, Shenzhen, Guangdong, 518172, China.
  • Chen Huang
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Ziji Liang
    Department of Urology, The Second Affiliated Hospital of The Chinese University of HongKong/Longgang District People's Hospital of Shenzhen, Shenzhen, Guangdong, 518172, China.
  • Chen Fan
    School of Data Science, Zhejiang University of Finance and Economics, Hangzhou, China.
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.
  • Yongquan Zhang
    Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China. Electronic address: zyqmath@163.com.
  • Xiang Wan
    Institute of Computational and Theoretical Study and Department of Computer Science, Hong Kong Baptist University, Hong Kong, P.R. China.
  • Changmiao Wang
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China.
  • Rong Hu
    College of Chemistry and Chemical Engineering, Yunnan Normal University , Yunnan, Kunming, 650092, People's Republic of China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Yi Jiang
    Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou 325035, China.
  • Yiwen Liang
    Department of Urology, The Second Affiliated Hospital of The Chinese University of HongKong/Longgang District People's Hospital of Shenzhen, Shenzhen, Guangdong, 518172, China.
  • Xingzhi Li
    Department of Urology, The Second Affiliated Hospital of The Chinese University of HongKong/Longgang District People's Hospital of Shenzhen, Shenzhen, Guangdong, 518172, China. xingzhi02459@gmail.com.

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