MIDI (MR Imaging Abnormality Deep Learning Identification)

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Deep Learning for Identification of Abnormalities on Head MRI

Conditions
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Neurological Disorder

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

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

Secondary Outcome Measures

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