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<p>Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic, relapsing condition with heterogeneous clinical phenotypes and variable therapeutic outcomes. Deep learning (DL), combined with high-throughput sequencing and multi-omics, has advanced precision management by enabling integration of genomic, transcriptomic, microbiome, metabolomic, imaging, and clinical data. DL applications include molecular biomarker discovery, automated endoscopic and histopathological image analysis, patient stratification, disease monitoring, and prediction of therapeutic response, such as anti-TNF-α efficacy. Convolutional neural networks (CNNs) demonstrate exceptional performance in image interpretation and automated scoring. Challenges for clinical translation include limited multi-center datasets, inconsistent annotations, low interpretability, and privacy concerns. Addressing these issues through interpretable, efficient, and privacy-preserving DL frameworks, along with temporally resolved cross-institutional datasets, will facilitate real-time monitoring and personalized care, reshaping IBD diagnosis, treatment, and long-term management.</p>

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Multimodal deep learning for inflammatory bowel disease: a new frontier in cellular and molecular biomarker discovery to clinical translation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月19日

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  • Peihong Li,
  • Siqing Guo,
  • Yikun Zhang,
  • Hongyi Hu,
  • TingJun Cheng,
  • Bo Xu,
  • Kexin Zeng,
  • Tianjiao Huang,
  • Zhi Dong,
  • BenHuo,
  • Jiang Lin,
  • Hongzhu Wen,
  • Boyun Sun

摘要

Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic, relapsing condition with heterogeneous clinical phenotypes and variable therapeutic outcomes. Deep learning (DL), combined with high-throughput sequencing and multi-omics, has advanced precision management by enabling integration of genomic, transcriptomic, microbiome, metabolomic, imaging, and clinical data. DL applications include molecular biomarker discovery, automated endoscopic and histopathological image analysis, patient stratification, disease monitoring, and prediction of therapeutic response, such as anti-TNF-α efficacy. Convolutional neural networks (CNNs) demonstrate exceptional performance in image interpretation and automated scoring. Challenges for clinical translation include limited multi-center datasets, inconsistent annotations, low interpretability, and privacy concerns. Addressing these issues through interpretable, efficient, and privacy-preserving DL frameworks, along with temporally resolved cross-institutional datasets, will facilitate real-time monitoring and personalized care, reshaping IBD diagnosis, treatment, and long-term management.