Clinical trial NCT04368481: MIDI (MR Imaging Abnormality Deep Learning Identification)
Official title: Deep Learning for Identification of Abnormalities on Head MRI
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Neurological Disorder
Lead sponsor: King's College Hospital NHS Trust
Sponsor class: OTHER
Collaborators: name: King's College London
First posted: 2020-04-29
Enrollment: 30000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All adult MRI head scan patients presenting at secondary and tertiary NHS centres across the UK for any indication.

Brief 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.

Detailed description: An automated strategy for identifying abnormalities in head scans could address the unmet clinical need for faster abnormality identification times, potentially allowing for early intervention to improve short- and long-term clinical outcomes. Radiologist shortages and increased demand for MRI scans lead to delays in reporting, particularly in the outpatient setting.

Furthermore, there is a wide variation in the management of incidental findings (IFs) discovered in 'healthy volunteers.' The routine reporting of 'healthy volunteer' scans by a radiologist poses logistical and financial challenges. It would be valuable to devise automated strategies to reliably and accurately identify IFs, potentially reducing the number of scans requiring routine radiological review by up to 90%, thus increasing the feasibility of implementing a routine reporting strategy.

Deep learning is a novel technique in computer science that automatically learns hierarchies of relevant features directly from the raw inputs (such as MRI or CT) using multi-layered neural networks. A deep learning algorithm will be trained on a large database of head MRI scans to recognize scans with abnormalities. This algorithm will be trained to classify a subset of these scans as normal or abnormal and then tested on an independent subset to determine its validity.

If the tested neural network demonstrates high diagnostic accuracy, future research participants and patients may benefit, as not all institutions currently review their research scans for incidental findings and clinical scans may not be reported for weeks in some cases. In both research and clinical scenarios, an algorithm could rapidly identify abnormal pathology and prioritize scans for reporting.

In summary, the aim is to develop a deep learning abnormality detection algorithm for use in both research and clinical settings.

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

Primary outcomes:
measure: Sensitivity and specificity of a convolutional neural network to recognise abnormalities on head MRI scans.
timeFrame: At end of study (5-year study)
Secondary outcomes:
measure: Sensitivity and specificity of a convolutional neural network to broadly categorise abnormalities on head MRI scans.
timeFrame: At end of study (5-year study)
Other outcomes:
Locations:
city: Orpington
country: United Kingdom
facility: Princess Royal University Hospital, King's College Hospital NHS Foundation Trust
state: Kent
city: Aylesbury
country: United Kingdom
facility: Buckinghamshire Healthcare Nhs Trust (Stoke Mandeville)
city: Basildon
country: United Kingdom
facility: Mid and South Essex NHS Foundation Trust
city: Bedford
country: United Kingdom
facility: Bedfordshire Hospitals Nhs Foundation Trust
city: Bodelwyddan
country: United Kingdom
facility: Betsi Cadwaladr University Health Board
city: Canterbury
country: United Kingdom
facility: East Kent Hospitals University Nhs Foundation Trust
city: Dundonald
country: United Kingdom
facility: South Eastern Health & Social Care Trust
city: East Grinstead
country: United Kingdom
facility: Queen Victoria Hospital Nhs Foundation Trust
city: Gillingham
country: United Kingdom
facility: Medway Nhs Foundation Trust
city: Grimsby
country: United Kingdom
facility: Northern Lincolnshire and Goole Nhs Foundation Trust
city: Huddersfield
country: United Kingdom
facility: Calderdale and Huddersfield NHS Foundation Trust
city: King's Lynn
country: United Kingdom
facility: The Queen Elizabeth Hospital King'S Lynn Nhs Trust
city: Kingston
country: United Kingdom
facility: Kingston Hospital Nhs Foundation Trust
city: Kirkcaldy
country: United Kingdom
facility: NHS FIFE
city: Larbert
country: United Kingdom
facility: Forth Valley Royal Hospital
city: Leeds
country: United Kingdom
facility: Leeds Teaching Hospital NHS Trust
city: Leicester
country: United Kingdom
facility: University Hospitals of Leicester Nhs Trust
city: London
country: United Kingdom
facility: Kings' College Hospital
city: London
country: United Kingdom
facility: CNS, Maudsley Hospital, South London and Maudsley NHS Foundation Trust
city: London
country: United Kingdom
facility: Croydon University Hospital, Croydon Health Services NHS Trust
city: London
country: United Kingdom
facility: Guy's Hospital, Guy's and St Thomas's NHS Foundation Trust
city: London
country: United Kingdom
facility: St George's Hospital, St George's University Hospital NHS Foundation Trust
city: London
country: United Kingdom
facility: St Thomas' Hospital, Guy's and St Thomas's NHS Foundation Trust
city: Norwich
country: United Kingdom
facility: Norfolk and Norwich University Hospitals Nhs Foundation Trust
city: Nottingham
country: United Kingdom
facility: Queen's Medical Centre University Hospital, Nottingham University Hospitals NHS Foundation Trust
city: Redhill
country: United Kingdom
facility: Surrey and Sussex Healthcare Nhs Trust
city: Saint Leonards-on-Sea
country: United Kingdom
facility: East Sussex Healthcare Nhs Trust
city: Scunthorpe
country: United Kingdom
facility: Northern Lincolnshire and Goole Nhs Foundation Trust
city: Southend
country: United Kingdom
facility: Mid and South Essex Nhs Foundation Trust
city: Tooting
country: United Kingdom
facility: St George'S University Hospitals Nhs Foundation Trust
city: Torquay
country: United Kingdom
facility: Torbay and South Devon Nhs Foundation Trust
city: Truro
country: United Kingdom
facility: Royal Cornwall Hospitals Nhs Trust
city: Watford
country: United Kingdom
facility: West Hertfordshire Hospitals Nhs Trust
Study officials:
affiliation: King's College Hospital NHS Trust
name: Thomas Booth
role: PRINCIPAL_INVESTIGATOR
References: