ICF: Using Explainable Artificial Intelligence to predict future stroke using routine historical investigations

ICF：使用可解释的人工智能通过常规历史调查来预测未来中风

• 批准号: MR/Y503472/1
• 负责人: Stephen Mullin
• 金额: $ 31.71万
• 依托单位: Plymouth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY503472%2F1
• 关键词: ICF; Using; Explainable; Artificial; Intelligence

There are more than 100,000 strokes in the UK each year causing 38,000 deaths, making it a leading cause of death and disability (NICE 2019). Ninety five percent of those who had a stroke had at least one untreated risk factor for it and 13.7% of these strokes were preventable. Findings that are predictive of future stroke are often identifiable on brain scans, electrocardiograms (ECG), heart scans (Echocardiogram or 'echo') and laboratory tests undertaken taken to investigate other medical problems. Often these signs are not picked up, which means evidence based treatments to reduce the risk of future stroke are not given. This is because of a lack of a dedicated system to do so. The first five years of care post stroke cost £3.60 billion (mean per patient cost: £46,039). Hence as well as improving many lives, a cost effective and accurate system to identify those at high risk of future stroke them will deliver major cost savings.Using 10,000 stroke cases seen at University Hospitals Plymouth NHS Trust (UHPNT) and with the close support of the Sentinel Stroke National Audit Programme (SSNAP - a national audit where all UK stroke cases are recorded), we hope to build a database of laboratory results, Magnetic resonance imaging (MRI) and computer tomography (CT) brain scans, ambulatory ECGs (ECGs which are worn for 24hr+) and echocardiograms, collected in those who later developed and did not develop a stroke. We shall use this data to train an artificial intelligence computer programme (model) which can predict who will later develop strokes based on patterns within the data collected. We believe that this approach will not only identify known risk factors for stroke, but may identify new patterns/features in one or across a number of investigations to predict future stroke. We hope this model will be the first step to building an automated system (which interfaces directly to GPs) for determining stroke risk and implementing treatments and lifestyle modifications to reduce this risk.

英国每年有超过10万例中风，造成3.8万人死亡，使其成为死亡和残疾的主要原因（NICE 2019）。95%的中风患者至少有一种未经治疗的风险因素，其中13.7%的中风是可以预防的。预测未来中风的发现通常可以通过脑部扫描、心电图（ECG）、心脏扫描（超声心动图或“回声”）和为调查其他医学问题而进行的实验室检查来识别。通常这些迹象没有被发现，这意味着没有给出基于证据的治疗方法来降低未来中风的风险。这是因为缺乏一个专门的系统来做这件事。中风后的头五年护理费用为36亿英镑（每位患者平均费用为46039英镑）。因此，除了改善许多人的生活，一个具有成本效益和准确的系统来识别未来中风的高风险人群将大大节省成本。利用普利茅斯大学医院NHS信托基金（UHPNT）看到的10,000例中风病例，并在哨兵中风国家审计计划（SSNAP -记录所有英国中风病例的国家审计）的密切支持下，我们希望建立一个实验室结果数据库，磁共振成像（MRI）和计算机断层扫描（CT）脑部扫描，动态心电图（24小时以上的心电图）和超声心动图，收集那些后来发展和没有发展中风的人。我们将使用这些数据来训练一个人工智能计算机程序（模型），该程序可以根据收集到的数据中的模式预测谁将在以后患上中风。我们相信这种方法不仅可以识别已知的中风危险因素，还可以在一项或多项研究中识别新的模式/特征，以预测未来的中风。我们希望这个模型将是建立一个自动化系统（直接与全科医生连接）的第一步，用于确定中风风险并实施治疗和改变生活方式以降低这种风险。