OpenClinicalAI: An open and dynamic model for Alzheimer’s Disease diagnosis

• Published in: Expert systems with applications Vol. 261; p. 125528
• Main Authors: Huang, Yunyou, Liang, Xiaoshuang, Xie, Jiyue, Lu, Xiangjiang, Miao, Xiuxia, Liu, Wenjing, Zhang, Fan, Kang, Guoxin, Ma, Li, Tang, Suqin, Zhan, Jianfeng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Alzheimer’s disease; Deep learning; Diagnose; Real-world clinical setting; Deep learning; AI; Alzheimer’s disease; Real-world clinical setting; Diagnose
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125528

Although Alzheimer’s disease (AD) cannot be reversed or cured, timely diagnosis can significantly reduce the burden of treatment and care. Current research on AD diagnosis models usually regards the diagnosis task as a typical classification task with two primary assumptions: (1) All target categories are known a priori; (2) The diagnostic strategy for each patient is consistent, that is, the number and type of model input data for each patient are the same. However, real-world clinical settings are open, with complexity and uncertainty in terms of both subjects and the resources of the medical institutions. This means that diagnostic models may encounter unseen disease categories and need to dynamically develop diagnostic strategies based on the subject’s specific circumstances and available medical resources. Thus, the AD diagnosis task is tangled and coupled with the diagnosis strategy formulation. To promote the application of diagnostic systems in real-world clinical settings, we propose OpenClinicalAI for direct AD diagnosis in complex and uncertain clinical settings. This is the first end-to-end model to dynamically formulate diagnostic strategies and provide diagnostic results based on the subject’s conditions and available medical resources. OpenClinicalAI combines reciprocally coupled deep multi-action reinforcement learning (DMARL) for diagnostic strategy formulation and multicenter meta-learning (MCML) for open-set recognition. The experimental results show that OpenClinicalAI achieves better performance and fewer clinical examinations than the state-of-the-art model. Our method provides an opportunity to embed the AD diagnostic system into the current healthcare system to cooperate with clinicians to improve current healthcare. •The first end to end model considering both AD diagnosis and strategy planning is proposed.•Model tailors diagnostic strategies and results based on patients and medical institution.•Our model achieved state-of-the-art performance in the simulated real-world clinical setting.