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.

在过去的十年中，由于人口统计学的变化，筛查的增加和临床路径中规定成像的更新临床指南，放射学工作量飙升。然而，只有2%的NHS信托基金符合2019年的报告要求。在COVID之前，在任何时候，330，000名患者等待MRI报告的时间超过30天-并且有预测称，由于需求超过放射科医生的可用性，这一时间将增加。COVID之后，成像积压增加了210%。报告延迟导致短期和长期的不良反应。根据世界卫生组织的调查结果，晚期发现疾病的长期临床结果增加了医疗费用。放射科医生经常从报告队列的后面报告，这加剧了识别异常的系统延迟。我们的项目制作了一种工具，可以立即标记大脑MRI异常，使放射科能够优先考虑有限的资源来报告异常扫描，从而加快转诊临床团队的早期干预，改善临床结果并降低医疗成本。此外，通过准确可靠的临床决策支持和报告积压，可能会减少平均扫描报告时间。具体的例子涉及癌症，中风或感染，早期诊断可以早期治疗。为了实现这一目标，我们提出了一种用于大脑MRI扫描的深度学习决策工具，该工具在英国既准确又可用。

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