MIDI (MR Imaging Abnormality Deep Learning Identification)

Recruiting

• Conditions: Neurological Disorder

• Registration Number: NCT04368481
• Lead Sponsor: King's College Hospital NHS Trust

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.

Study Timeline

• Study Start: 2019-04-01
• Study First Submitted: 2020-02-18
• Study First Submitted QC: 2020-04-27
• Study First Posted: 2020-04-29
• Status Verified: 2024-04
• Last Update Submitted: 2024-04-08
• Last Update Posted: 2024-04-10
• Primary Completion: 2024-08-31
• Study Completion: 2025-03-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 30000

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Sensitivity and specificity of a convolutional neural network to recognise abnormalities on head MRI scans.	At end of study (5-year study)	Sensitivity, specificity, positive predictive value, and negative predictive values.

Secondary Outcome Measures

Name	Time	Method
Sensitivity and specificity of a convolutional neural network to broadly categorise abnormalities on head MRI scans.	At end of study (5-year study)	Sensitivity, specificity, positive predictive value, and negative predictive values.

Trial Locations

Locations (32)

Princess Royal University Hospital, King's College Hospital NHS Foundation Trust; 🇬🇧 Orpington, Kent, United Kingdom

Buckinghamshire Healthcare Nhs Trust (Stoke Mandeville); 🇬🇧 Aylesbury, United Kingdom

Mid and South Essex NHS Foundation Trust; 🇬🇧 Basildon, United Kingdom

Bedfordshire Hospitals Nhs Foundation Trust; 🇬🇧 Bedford, United Kingdom

Betsi Cadwaladr University Health Board; 🇬🇧 Bodelwyddan, United Kingdom

East Kent Hospitals University Nhs Foundation Trust; 🇬🇧 Canterbury, United Kingdom

South Eastern Health & Social Care Trust; 🇬🇧 Dundonald, United Kingdom

Queen Victoria Hospital Nhs Foundation Trust; 🇬🇧 East Grinstead, United Kingdom

Medway Nhs Foundation Trust; 🇬🇧 Gillingham, United Kingdom

Northern Lincolnshire and Goole Nhs Foundation Trust; 🇬🇧 Scunthorpe, United Kingdom

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