Magnetic resonance Imaging abnormality Deep learning Identification (MIDI)

点击翻译按钮获取中文摘要

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.