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<p>Idiopathic pulmonary fibrosis (IPF) is a progressive and ultimately fatal disorder of unknown etiology, characterized by interstitial fibrosis of the lungs. Bleomycin-induced pulmonary fibrosis mouse model (BLM model) is a widely used animal model to evaluate therapeutic targets for IPF. Histopathological analysis of lung fibrosis is an important method for evaluating BLM model. However, this method requires expertise in recognizing complex visual patterns and is time-consuming, making the workflow difficult and inefficient. Therefore, we developed a new workflow for BLM model that reduces inter- and intra-observer variations and improves the evaluation process. We generated deep learning models for grading lung fibrosis that were able to achieve accuracy comparable to that of pathologists. These models incorporate complex image patterns and qualitative factors, such as collagen texture and distribution, potentially identifying drug candidates overlooked in evaluations based solely on simple area extraction. This deep learning-based fibrosis grade assessment has the potential to streamline drug development for pulmonary fibrosis by offering higher granularity and reproducibility in evaluating BLM model.</p>

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Optimized digital workflow for pathologist-grade evaluation in bleomycin-induced pulmonary fibrosis mouse model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月17日

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  • Toshiki Goto,
  • Akira Sano,
  • Shinichi Onishi,
  • Natsuko Hada,
  • Rui Kimata,
  • Saori Matsuo,
  • Sohei Oyama,
  • Atsuhiko Kato,
  • Hideaki Mizuno,
  • Masaki Yamazaki

摘要

Idiopathic pulmonary fibrosis (IPF) is a progressive and ultimately fatal disorder of unknown etiology, characterized by interstitial fibrosis of the lungs. Bleomycin-induced pulmonary fibrosis mouse model (BLM model) is a widely used animal model to evaluate therapeutic targets for IPF. Histopathological analysis of lung fibrosis is an important method for evaluating BLM model. However, this method requires expertise in recognizing complex visual patterns and is time-consuming, making the workflow difficult and inefficient. Therefore, we developed a new workflow for BLM model that reduces inter- and intra-observer variations and improves the evaluation process. We generated deep learning models for grading lung fibrosis that were able to achieve accuracy comparable to that of pathologists. These models incorporate complex image patterns and qualitative factors, such as collagen texture and distribution, potentially identifying drug candidates overlooked in evaluations based solely on simple area extraction. This deep learning-based fibrosis grade assessment has the potential to streamline drug development for pulmonary fibrosis by offering higher granularity and reproducibility in evaluating BLM model.