Using artificial intelligence to mitigate scanner bias in brain disorders

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Brain-based disorders represent 14% of the global burden of disease. Artificial Intelligence (AI) applications to neuroimaging have the potential to improve the detection and treatment of these disorders. This potential has been demonstrated in controlled research setups, but the real-world clinical implementation of these techniques is limited by differences in scanning equipment and protocols. These differences, known as scanner bias, means that diagnostic and prognostic AI applications developed using images from a certain machine tend to perform poorly when applied to images from different machines. There is preliminary evidence that some AI-based approaches can mitigate this problem, however, they are limited to healthy subjects. We will adapt and validate Neuroharmony, an AI-based solution for this problem, using data from clinical populations with psychiatric or neurological disorders obtained across multiple machines. The results obtained here will improve Neuroharmony, validate it for clinical application and make it available to the research and clinical communities. This tool can help bridge the gap between AI-based research and clinical practice.