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

 An automated strategy could also address the unmet clinical need in identifying abnormalities
 quicker, 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.

 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.
Inclusion Criteria:

 -  All head MRI scans with compatible sequences

 -  > 18 years old

 Exclusion Criteria:

 -  No corresponding radiologist report for the retrospective analyses

 -  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