A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.

Journal: Abdominal radiology (New York)
PMID: 38703189

Abstract

OBJECTIVES: Differentiating intestinal tuberculosis (ITB) from Crohn's disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach for distinguishing ITB from CD.

Authors

  • Baolan Lu
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.
  • Zengan Huang
    Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, People's Republic of China.
  • Jinjiang Lin
    Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, 510080, Guangdong, People's Republic of China.
  • Ruonan Zhang
    Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, 510080, Guangdong, People's Republic of China.
  • Xiaodi Shen
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.
  • Lili Huang
    Department of Endocrinology, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
  • Xinyue Wang
    Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China.
  • Weitao He
    Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, 510080, Guangdong, People's Republic of China.
  • Qiapeng Huang
    Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, People's Republic of China.
  • Jiayu Fang
    Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, 510080, Guangdong, People's Republic of China.
  • Ren Mao
    Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Zhoulei Li
    Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, 510080, Guangdong, People's Republic of China.
  • Bingsheng Huang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Shi-Ting Feng
    Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Ziying Ye
    Department of Pathology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, People's Republic of China. yeziyin@mail.sysu.edu.cn.
  • Jian Zhang
    College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.
  • Yangdi Wang
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People's Republic of China.

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