A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data
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 complicat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58801-7 |
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| _version_ | 1849310321257218048 |
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| author | 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 Shi Gu |
| author_facet | 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 Shi Gu |
| author_sort | Yifan Wu |
| collection | DOAJ |
| description | 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 F 1 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. |
| format | Article |
| id | doaj-art-e69f1e69bf9c4bafa8acd37e6ee6be86 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e69f1e69bf9c4bafa8acd37e6ee6be862025-08-20T03:53:46ZengNature PortfolioNature Communications2041-17232025-04-0116111410.1038/s41467-025-58801-7A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal dataYifan Wu0Yang Liu1Yue Yang2Michael S. Yao3Wenli Yang4Xuehui Shi5Lihong Yang6Dongjun Li7Yueming Liu8Shiyi Yin9Chunyan Lei10Meixia Zhang11James C. Gee12Xuan Yang13Wenbin Wei14Shi Gu15University of PennsylvaniaUniversity of Electronic Science and Technology of ChinaUniversity of PennsylvaniaUniversity of PennsylvaniaBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical UniversityDepartment of Ophthalmology and Research Laboratory of Macular Disease, West China Hospital, Sichuan UniversityDepartment of Ophthalmology and Research Laboratory of Macular Disease, West China Hospital, Sichuan UniversityUniversity of PennsylvaniaBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical UniversityUniversity of Electronic Science and Technology of ChinaAbstract 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 F 1 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.https://doi.org/10.1038/s41467-025-58801-7 |
| spellingShingle | 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 Shi Gu A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data Nature Communications |
| title | A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data |
| title_full | A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data |
| title_fullStr | A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data |
| title_full_unstemmed | A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data |
| title_short | A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data |
| title_sort | concept based interpretable model for the diagnosis of choroid neoplasias using multimodal data |
| url | https://doi.org/10.1038/s41467-025-58801-7 |
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