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

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机构: [1]University of Pennsylvania, Philadelphia, PA, USA. [2]University of Electronic Science and Technology of China, Chengdu, China. [3]Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China. [4]Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China. [5]Beijing Ophthalmology and Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China. [6]Department of Ophthalmology and Research Laboratory of Macular Disease, West China Hospital, Sichuan University, Chengdu, China. [7]College of Computer Science and Technology, Zhejiang University, Hangzhou, China. [8]State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China.
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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.© 2025. The Author(s).

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出版当年[2025]版:
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
最新[2025]版:
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
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出版当年[2023]版:
Q1 MULTIDISCIPLINARY SCIENCES
最新[2024]版:
Q1 MULTIDISCIPLINARY SCIENCES

影响因子: 最新[2024版] 最新五年平均 出版当年[2023版] 出版当年五年平均 出版前一年[2022版] 出版后一年[2024版]

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第一作者机构: [1]University of Pennsylvania, Philadelphia, PA, USA.
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通讯机构: [2]University of Electronic Science and Technology of China, Chengdu, China. [3]Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China. [4]Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China. [5]Beijing Ophthalmology and Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China. [7]College of Computer Science and Technology, Zhejiang University, Hangzhou, China. [8]State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China.
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