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A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data

Author

Listed:
  • Yifan Wu

    (University of Pennsylvania)

  • Yang Liu

    (University of Electronic Science and Technology of China)

  • Yue Yang

    (University of Pennsylvania)

  • Michael S. Yao

    (University of Pennsylvania)

  • Wenli Yang

    (Capital Medical University
    Capital Medical University
    Capital Medical University)

  • Xuehui Shi

    (Capital Medical University
    Capital Medical University
    Capital Medical University)

  • Lihong Yang

    (Capital Medical University
    Capital Medical University
    Capital Medical University)

  • Dongjun Li

    (Capital Medical University
    Capital Medical University
    Capital Medical University)

  • Yueming Liu

    (Capital Medical University
    Capital Medical University
    Capital Medical University)

  • Shiyi Yin

    (Capital Medical University
    Capital Medical University
    Capital Medical University)

  • Chunyan Lei

    (Sichuan University)

  • Meixia Zhang

    (Sichuan University)

  • James C. Gee

    (University of Pennsylvania)

  • Xuan Yang

    (Capital Medical University
    Capital Medical University
    Capital Medical University)

  • Wenbin Wei

    (Capital Medical University
    Capital Medical University
    Capital Medical University)

  • Shi Gu

    (University of Electronic Science and Technology of China
    Zhejiang University
    Zhejiang University)

Abstract

Diagnosing rare diseases remains a critical challenge in clinical practice, often requiring specialist expertise. Despite the promising potential of machine learning, the scarcity of data on rare diseases and the need for interpretable, reliable artificial intelligence (AI) models complicates development. This study introduces a multimodal concept-based interpretable model tailored to distinguish uveal melanoma (0.4-0.6 per million in Asians) from hemangioma and metastatic carcinoma following the clinical practice. We collected a comprehensive dataset on Asians to date on choroid neoplasm imaging with radiological reports, encompassing over 750 patients from 2013 to 2019. Our model integrates domain expert insights from radiological reports and differentiates between three types of choroidal tumors, achieving an F1 score of 0.91. This performance not only matches senior ophthalmologists but also improves the diagnostic accuracy of less experienced clinicians by 42%. The results underscore the potential of interpretable AI to enhance rare disease diagnosis and pave the way for future advancements in medical AI.

Suggested Citation

  • Yifan Wu & Yang Liu & Yue Yang & Michael S. Yao & Wenli Yang & Xuehui Shi & Lihong Yang & Dongjun Li & Yueming Liu & Shiyi Yin & Chunyan Lei & Meixia Zhang & James C. Gee & Xuan Yang & Wenbin Wei & Sh, 2025. "A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58801-7
    DOI: 10.1038/s41467-025-58801-7
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    References listed on IDEAS

    as
    1. Xiaoman Zhang & Chaoyi Wu & Ya Zhang & Weidi Xie & Yanfeng Wang, 2023. "Knowledge-enhanced visual-language pre-training on chest radiology images," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Mingquan Lin & Tianhao Li & Yifan Yang & Gregory Holste & Ying Ding & Sarah H. Tassel & Kyle Kovacs & George Shih & Zhangyang Wang & Zhiyong Lu & Fei Wang & Yifan Peng, 2023. "Improving model fairness in image-based computer-aided diagnosis," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
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