Magnetic resonance Imaging abnormality Deep learning Identification (MIDI)

在过去的十年中，由于人口结构的变化、筛查的增加和临床路径中指定成像的临床指南的更新，放射学工作量激增。然而，只有2%的NHS信托基金达到了2019年的报告要求。在新冠肺炎之前，在任何时候，都有33万名患者等待30天的核磁共振报告——有人预测，由于需求大于放射科医生的可用性，这一数字将会增加。在COVID之后，成像积压增加了210%。根据世界卫生组织的调查结果，报告延迟会导致较差的短期和长期临床结果，疾病发现较晚会增加医疗成本。放射科医生通常在报告队列的后面报告，导致识别异常的系统性延迟，这加剧了这种情况。我们的项目制作了一种工具，可以立即标记大脑MRI异常，使放射科能够优先分配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，早期诊断可以使早期治疗成为可能。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。

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.