Try the modernized ClinicalTrials.gov beta website. Learn more about the modernization effort.
Working…
ClinicalTrials.gov
ClinicalTrials.gov Menu

MIDI (MR Imaging Abnormality Deep Learning Identification) (MIDI)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04368481
Recruitment Status : Recruiting
First Posted : April 29, 2020
Last Update Posted : July 9, 2021
Sponsor:
Collaborator:
King's College London
Information provided by (Responsible Party):
King's College Hospital NHS Trust

Brief Summary:
The study involves the development and testing of an AI tool that can identify abnormalities using patient head scans carried out 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.

Condition or disease
Neurological Disorder

Detailed Description:

An automated strategy for identifying abnormalities on head scans could address the unmet clinical need of 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 mean delays in reporting, particularly in the outpatient setting.

In addition, there is a wide variation in how incidental findings (IFs) discovered in 'healthy volunteers' are managed. Routine reporting of 'healthy volunteer' scans by a radiologist is a challenging logistic and financial burden. It would be valuable to devise automated strategies to ensure that IFs can be reliably and accurately identified potentially removing 90% of scans requiring routine radiological review, thereby increasing the feasibility of implementing a routine reporting strategy.

Deep learning is a new 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 recognise scans with abnormalities. This algorithm will be trained to classify a subset of these scans as normal or abnormal. The technique will then be tested on an independent subset to determine its validity.

If the tested neural network has a high diagnostic accuracy, future research participants may benefit as currently not all institutions review their research scans for incidental findings. Similarly, in those cases where clinical scans may not be reported for weeks, patients may benefit. In both research and clinical scenarios, an algorithm would quickly identify abnormal pathology and prioritise scans for reporting.

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

Layout table for study information
Study Type : Observational
Estimated Enrollment : 30000 participants
Observational Model: Other
Time Perspective: Other
Official Title: Deep Learning for Identification of Abnormalities on Head MRI
Actual Study Start Date : April 1, 2019
Estimated Primary Completion Date : October 31, 2023
Estimated Study Completion Date : November 20, 2023



Primary Outcome Measures :
  1. Sensitivity and specificity of a convolutional neural network to recognise abnormalities on head MRI scans. [ Time Frame: At end of study (5-year study) ]
    Sensitivity, specificity, positive predictive value, and negative predictive values.


Secondary Outcome Measures :
  1. Sensitivity and specificity of a convolutional neural network to broadly categorise abnormalities on head MRI scans. [ Time Frame: At end of study (5-year study) ]
    Sensitivity, specificity, positive predictive value, and negative predictive values.



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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.
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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04368481


Contacts
Layout table for location contacts
Contact: MIDI Central Team +44(0)20 7848 9670 kch-tr.midistudy@nhs.net

Locations
Layout table for location information
United Kingdom
Princess Royal University Hospital, King's College Hospital NHS Foundation Trust Recruiting
Orpington, Kent, United Kingdom
Contact: MIDI Research Team    020 3228 3035    kch-tr.midistudy@nhs.net   
Sub-Investigator: Ajay Arora         
Calderdale and Huddersfield NHS Foundation Trust Recruiting
Huddersfield, United Kingdom
Contact: Hannah Riley    01484 347165    hannah.riley2@cht.nhs.uk   
Principal Investigator: Georgina Turner         
CNS, Maudsley Hospital, South London and Maudsley NHS Foundation Trust Recruiting
London, United Kingdom
Contact: MIDI Research Team    020 3228 3035    kch-tr.midistudy@nhs.net   
Principal Investigator: Thomas Booth         
Croydon University Hospital, Croydon Health Services NHS Trust Recruiting
London, United Kingdom
Contact: Croydon Research Team    020 8401 3000 ext 3829/5279    ch-tr.midicroydon@nhs.net   
Principal Investigator: Ketul Patel         
Guy's Hospital, Guy's and St Thomas's NHS Foundation Trust Recruiting
London, United Kingdom
Contact: MIDI Research Team    020 3228 3035    kch-tr.midistudy@nhs.net   
Principal Investigator: Asif Mazumdar         
King's College Hospital, King's College Hospital NHS Foundation Trust Recruiting
London, United Kingdom
Contact: MIDI KCH Research Team       kch-tr.midistudy@nhs.net   
Principal Investigator: Thomas Booth         
St George's Hospital, St George's University Hospital NHS Foundation Trust Recruiting
London, United Kingdom
Contact: Naomi Priestley    020 8725 3260    naomi.priestley@stgeorges.nhs.uk   
Principal Investigator: Andrew Mackinnon         
St Thomas' Hospital, Guy's and St Thomas's NHS Foundation Trust Recruiting
London, United Kingdom
Contact: MIDI Research Team    020 3228 3035    kch-tr.midistudy@nhs.net   
Principal Investigator: Asif Mazumdar         
Queen's Medical Centre University Hospital, Nottingham University Hospitals NHS Foundation Trust Recruiting
Nottingham, United Kingdom
Contact: NUH MIDI Research Team       midi@nuh.nhs.uk   
Principal Investigator: Rob Dineen         
Sub-Investigator: Carolyn Costigan         
Sponsors and Collaborators
King's College Hospital NHS Trust
King's College London
Investigators
Layout table for investigator information
Principal Investigator: Thomas Booth King's College Hospital NHS Trust
Layout table for additonal information
Responsible Party: King's College Hospital NHS Trust
ClinicalTrials.gov Identifier: NCT04368481    
Other Study ID Numbers: KCH18-197
First Posted: April 29, 2020    Key Record Dates
Last Update Posted: July 9, 2021
Last Verified: July 2021

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
Layout table for MeSH terms
Nervous System Diseases