A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data
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
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. Diagnosing rare diseases, such as choroid neoplasias, remains a critical challenge. Here, the authors develop a multimodal concept-based interpretable model (MMCBM) to distinguish uveal melanoma from hemangioma and metastatic carcinoma, obtaining performance comparable to senior ophthalmologists in a large cohort of Asian patients with choroid neoplasms.