MIDI (MR Imaging Abnormality Deep Learning Identification) (MIDI)
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|ClinicalTrials.gov Identifier: NCT04368481|
Recruitment Status : Recruiting
First Posted : April 29, 2020
Last Update Posted : July 28, 2022
|Condition or disease|
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.
|Study Type :||Observational|
|Estimated Enrollment :||30000 participants|
|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|
- 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.
- 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.
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
|Contact: MIDI Central Team||+44(0)20 7848 firstname.lastname@example.org|
|Principal Investigator:||Thomas Booth||King's College Hospital NHS Trust|