Artificial intelligence for predicting interstitial fibrosis and tubular atrophy using diagnostic ultrasound imaging and biomarkers.

Journal: BMJ health & care informatics
PMID: 40097202

Abstract

BACKGROUND: Chronic kidney disease (CKD) is a global health concern characterised by irreversible renal damage that is often assessed using invasive renal biopsy. Accurate evaluation of interstitial fibrosis and tubular atrophy (IFTA) is crucial for CKD management. This study aimed to leverage machine learning (ML) models to predict IFTA using a combination of ultrasonography (US) images and patient biomarkers.

Authors

  • Ting-Wei Chang
    Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
  • Chang-Yu Tsai
    Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Zhen-Yi Tang
    Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Cai-Mei Zheng
    Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Chia-Te Liao
    Division of Cardiovascular Medicine, Chi Mei Medical Center, Tainan, Taiwan; Evidence-Based Medicine and Health Policy Center, Chi Mei Medical Center, Tainan, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan. Electronic address: Drctliao@gmail.com.
  • Chung-Yi Cheng
    Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Mai-Szu Wu
    Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan; Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Che-Chou Shen
    Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
  • Yen-Chung Lin
    Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan yclin0229@tmu.edu.tw.

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