Magnetic resonance Imaging abnormality Deep learning Identification (MIDI)

磁共振成像异常深度学习识别（MIDI）

• 批准号: MR/W021684/1
• 负责人: Thomas Booth
• 金额: $ 141.26万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW021684%2F1
• 关键词: Magnetic; resonance; Imaging; abnormality; Deep

Radiology workloads soared over the past decade due to changing demographics, increased screening and updated clinical guidelines specifying imaging in the clinical pathway. However, only 2% of NHS trusts met their 2019 reporting requirements. Before COVID, at any one time, 330,000 patients wait >30 days for their MRI reports - and there were forecasts that this would increase due to more demand than radiologist availability.Following COVID there has been a 210% increase in the imaging backlog.Reporting delays cause poorer short- and long-term clinical outcomes with late detection of illness inflating healthcare costs in accordance with World Health Organisation findings. This is exacerbated by radiologists routinely reporting from the back of report queues, introducing systemic delay in identifying abnormalities. Our project makes a tool that will immediately flag brain MRI abnormalities allowing radiology departments to prioritise limited resources into reporting abnormal scans, thereby expediting early intervention from the referring clinical team, improving clinical outcomes and lowering healthcare costs. Furthermore, there will likely be reductions in mean-scan-reporting-time, through accurate and reliable clinical decision support, and reporting backlogs. Specific examples relate to cancer, stroke or infection where an early diagnosis can allow early treatment.To achieve this we propose a deep learning decision-making tool for brain MRI scans that is both accurate and usable across the UK.

由于人口统计的变化，筛查的增加和更新的临床指南，在过去的十年中，放射学工作负载飙升。但是，只有2％的NHS信托符合其2019年的报告要求。在COVID之前，有330,000名患者等待30天的MRI报告 - 预测，由于需求比放射科医生的可用性更多，这会增加。依次提高Imaging Backlog的增长210％。报告延迟延误会导致较短期和长期临床的临床临床，并且由于疾病的生病成本而导致较晚的临床造成的临床效果，因此健康的临床成本累积了。放射科医生通常会从报告队列的背面报告，这使这加剧了这一点，从而引入了系统性延迟识别异常。我们的项目制造了一种工具，它将立即提高大脑MRI异常，从而使放射学部门可以优先考虑有限的资源来报告异常扫描，从而加快了转诊临床团队的早期干预，改善了临床结果并降低了医疗保健成本。此外，通过准确可靠的临床决策支持以及报告积压，可能会降低均匀报告的时间。与癌症，中风或感染有关的具体例子，即早期诊断可以允许早期治疗。为了实现这一目标，我们提出了一个深度学习决策工具，用于大脑MRI扫描，这在整个英国既准确又可用。

项目成果

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis

深度学习模型在硬膜下血肿检测中的应用：系统评价和荟萃分析

• DOI: 10.1136/jnis-2023-020218
• 发表时间: 2023
• 期刊: Journal of NeuroInterventional Surgery
• 影响因子: 4.8
• 作者: Agarwal S

Federated learning enables big data for rare cancer boundary detection.

• DOI: 10.1038/s41467-022-33407-5
• 发表时间: 2022-12-05
• 期刊: Nature communications
• 影响因子: 16.6

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.

• DOI: 10.1136/jnis-2022-019456
• 发表时间: 2023-03
• 期刊: Journal of neurointerventional surgery
• 影响因子: 4.8