AI-PROGNOSIS_Artificial intelligence-based Parkinson’s disease risk assessment and prognosis

帕金森病（PD）是最常见的神经退行性运动障碍，具有多因素病因，运动和非运动症状的异质性表现，并且无法治愈。PD经常被遗漏或误诊，因为早期症状很微妙，与其他疾病一样常见，在治疗前就会出现相当大的损害。此外，选择最佳的药物治疗方案通常是一个漫长的“试错”过程，导致严重的、代价高昂的不依从。AI- prognosis采用值得信赖和包容的人工智能开发方法，基于多学科专业知识和广泛的利益相关者参与，旨在通过以下方式推进帕金森病的诊断和护理：1)基于多源患者记录和数据库，包括深入的健康、表型和遗传数据，开发用于个性化PD风险评估和预后（从时间到更高的残疾过渡和对药物的反应）的新型预测性人工智能模型；2)通过跟踪日常生活中的关键风险/进展标志物，实现生物标志物系统，为人工智能模型提供信息；具有隐私意识的人工智能驱动工具包，通过定量的、可解释的证据支持医疗保健专业人员（HCPs）进行疾病筛查、监测和治疗优化，并为患有/非PD的个人提供量身定制的见解，以实现知情的健康管理。

Parkinson’s disease (PD) is the most common neurodegenerative movement disorder, with a multifactorial aetiology, heterogeneous manifestation of motor and non-motor symptoms, and no cure. PD is often missed or misdiagnosed, as early symptoms are subtle and common with other diseases, allowing for considerable damage to occur before treatment. Moreover, selecting the optimal medication regimen is usually a lengthy, “trial and error” process, leading to critical, costly non-adherence. Following a trustworthy and inclusive approach to AI development and based on multidisciplinary expertise and broad stakeholder engagement, AI-PROGNOSIS aims to advance PD diagnosis and care by: 1) developing novel, predictive AI models for personalised PD risk assessment and prognosis (in terms of time to higher disability transition and response to medication) based on multi-source patient records and databases, including in-depth health, phenotypic and genetic data, 2) implementing a system of biomarkers informing the AI models by tracking key risk/ progression markers in daily living, and ultimately 3) translating the models and digital biomarkers into a validated, privacy-aware AI driven toolkit, supporting healthcare professionals (HCPs) in disease screening, monitoring and treatment optimization via quantitative, explainable evidence, and empowering individuals with/without PD with tailored insights for informed health management.