AI-Assisted Semiquantitative Measurement of Murine Bleomycin-Induced Lung Fibrosis Using In Vivo Micro-CT: An End-to-End Approach.

Journal: American journal of physiology. Cell physiology
Published Date: Jul 21, 2025

Abstract

Small animal models are crucial for investigating idiopathic pulmonary fibrosis (IPF) and developing preclinical therapeutic strategies. However, there are several limitations to the quantitative measurements used in the longitudinal assessment of experimental lung fibrosis, e.g., histological or biochemical analyses introduce inter-individual variability, while image-derived biomarker has yet to directly and accurately quantify the severity of lung fibrosis. This study investigates artificial intelligence (AI)-assisted, end-to-end, semi-quantitative measurement of lung fibrosis using in vivo micro-CT. Based on the bleomycin (BLM)-induced lung fibrosis mouse model, the AI model predicts histopathological scores from in vivo micro-CT images, directly correlating these images with the severity of lung fibrosis in mice. Fibrosis severity was graded by the Ashcroft scale: none (0), mild (1-3), moderate (4-5), severe (≥6).The overall accuracy, precision, recall, and F1 scores of the lung fibrosis severity-stratified 3-fold cross validation on 225 micro-CT images for the proposed AI model were 92.9%, 90.9%, 91.6%, and 91.0%. The overall area under the receiver operating characteristic curve (AUROC) was 0.990 (95% CI: 0.977, 1.000), with AUROC values of 1.000 for none (100 images, 95% CI: 0.997, 1.000), 0.969 for mild (43 images, 95% CI: 0.918, 1.000), 0.992 for moderate (36 images, 95% CI: 0.962, 1.000), and 0.992 for severe (46 images, 95% CI: 0.967, 1.000). Preliminary results indicate that AI-assisted, in vivo micro-CT-based semi-quantitative measurements of murine are feasible and likely accurate. This novel method holds promise as a tool to improve the reproducibility of experimental studies in animal models of IPF.

Authors

  • Hanlin Cheng
    School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China.
  • Tianyun Gao
    School of Medicine, Southeast University, Nanjing, China.
  • Yichen Sun
    Department of Neurosurgery, Huashan Hospital, Fudan University, 12 Wulumuqi Road (M), Shanghai, 200040, China.
  • Feifei Huang
    School of Nursing, Fujian Medical University, Fuzhou, 350122, Fujian, China.
  • Xiaohui Gu
    Department of Networking and Edge Division, Intel Asia Pacific Research and Development, Shanghai, Shanghai, China.
  • Chunjie Shan
    School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Shouhua Luo

Keywords

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