Deep Learning for Identification of Abnormalities on Head MRI
Trial ID: NCT04368481
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-04-01
Completion date: 2025-03-31
Sponsor: King's College Hospital NHS Trust
Collaborators: King's College London
Conditions: Neurological Disorder
Locations: Princess Royal University Hospital, King's College Hospital NHS Foundation Trust; Buckinghamshire Healthcare Nhs Trust (Stoke Mandeville); Mid and South Essex NHS Foundation Trust; Bedfordshire Hospitals Nhs Foundation Trust; Betsi Cadwaladr University Health Board; East Kent Hospitals University Nhs Foundation Trust; South Eastern Health & Social Care Trust; Queen Victoria Hospital Nhs Foundation Trust; Medway Nhs Foundation Trust; Northern Lincolnshire and Goole Nhs Foundation Trust; Calderdale and Huddersfield NHS Foundation Trust; The Queen Elizabeth Hospital King'S Lynn Nhs Trust; Kingston Hospital Nhs Foundation Trust; NHS FIFE; Forth Valley Royal Hospital; Leeds Teaching Hospital NHS Trust; University Hospitals of Leicester Nhs Trust; Kings' College Hospital; CNS, Maudsley Hospital, South London and Maudsley NHS Foundation Trust; Croydon University Hospital, Croydon Health Services NHS Trust; Guy's Hospital, Guy's and St Thomas's NHS Foundation Trust; St George's Hospital, St George's University Hospital NHS Foundation Trust; St Thomas' Hospital, Guy's and St Thomas's NHS Foundation Trust; Norfolk and Norwich University Hospitals Nhs Foundation Trust; Queen's Medical Centre University Hospital, Nottingham University Hospitals NHS Foundation Trust; Surrey and Sussex Healthcare Nhs Trust; East Sussex Healthcare Nhs Trust; Northern Lincolnshire and Goole Nhs Foundation Trust; Mid and South Essex Nhs Foundation Trust; St George'S University Hospitals Nhs Foundation Trust; Torbay and South Devon Nhs Foundation Trust; Royal Cornwall Hospitals Nhs Trust; West Hertfordshire Hospitals Nhs Trust
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 30000
Age range: 18 Years - None
Sex: ALL

Summary:
The study involves the development and testing of an artificial intelligence (AI) tool that can identify abnormalities using patient head scans conducted for routine clinical care and research volunteer scans. A deep learning algorithm will be developed using a dataset of retrospective and prospective MRI head scans to train, validate, and test convolutional networks using software developed at the Department of Biomedical Engineering, King's College London. The reference standard will be consultant radiologist reports of the MRI head scans.

Eligibility criteria:
Inclusion Criteria:

* All head MRI scans with compatible sequences
* \> 18 years old

Exclusion Criteria:

* No corresponding radiologist report
* No consent for future use of the research images held within the historic database stored at The Centre for Neuroimaging Sciences (Kings College London).
* Poor image quality

Outcomes:
Sensitivity and specificity of a convolutional neural network to recognise abnormalities on head MRI scans.