Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis.

Journal: European journal of medical research
PMID: 40235000

Abstract

BACKGROUND: The diagnosis of idiopathic pulmonary fibrosis (IPF) is complex, which requires lung biopsy, if necessary, and multidisciplinary discussions with specialists. Clinical diagnosis of the two ailments is particularly challenging due to the impact of interobserver variability. Several studies have endeavored to utilize image-based machine learning to diagnose IPF and its subtype of usual interstitial pneumonia (UIP). However, the diagnostic accuracy of this approach lacks evidence-based support.

Authors

  • Li Cong
    School of Medicine, Hunan Normal University, Changsha, China.
  • Ying Chen
    Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Xiaolei He
    Radiographic Center, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
  • MaiLiKai KuErBan
    Radiographic Center, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
  • Chao Wu
  • Liping Chen
    Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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