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.

英国每年发生超过 100,000 起中风，导致 38,000 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危险因素，13.7% 的中风患者有至少一种未经治疗的危险因素。这些中风的预测通常可以通过脑部扫描、心电图 (ECG)、心脏扫描（超声心动图或超声心动图）来识别。 “回声”）和针对其他医疗问题进行的实验室测试通常不会发现这些迹象，这意味着没有对降低未来中风风险的基于证据的治疗进行研究，这是因为缺乏专门的系统。因此，中风后前五年的护理费用为 36 亿英镑（平均每名患者的费用为 46,039 英镑）。主要节省成本。使用在普利茅斯 NHS 信托大学医院 (UHPNT) 观察到的 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（佩戴 24 小时以上的心电图）和超声心动图我们将使用这些数据来训练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来会发生中风。我们相信这种方法不仅可以识别已知的风险。中风的因素，但可能会在一项或多项调查中识别出新的模式/特征来预测未来的中风，我们希望该模型将成为构建中风风险确定和预测的自动化系统（直接与全科医生连接）的第一步。实施治疗和生活方式改变以降低这种风险。