SLaM Image Bank – a real-world diverse London cohort linking brain MRI to electronic mental health and dementia records for the development of clinical decision support tools using artificial intelligence
Authors: Venkataraman Ashwin V. , Wright Paul , Jewell Amelia , Webb Christopher , Zhang Yini , Patil Soha K. , Cardoso M. Jorge , Cole James H. , Kempton Matthew J. , Marshall Charles R. , Mehta Mitul A. , Mueller Christoph , Nicholson Timothy R. , Pollak Thomas A. , Rittman Timothy , Sahin Zeynep , Solana Ana B. , Stahl Daniel R. , Váša František , Young Allan H , Williams Steve , Stewart Robert , Aarsland Dag 
Published: January 2025

Purpose

Rapid developments are occurring in artificial intelligence (AI) and machine learning (ML) applied to neuroimaging. To date, advances in this space have largely been limited to research cohorts with little real-world translation that is clinically meaningful for patients in psychiatry and neurology and those with associated neuropsychiatric symptoms. There is a lack of large real-world multimodal linked datasets combining MRI imaging, clinical variables and other biomarkers within more representative ethnically diverse and deprived populations with multiple neuropsychiatric and systemic co-morbidities, alongside the right infrastructure to link them.

Participants

We linked brain MRI scans in South London and Maudsley (SLaM) NHS Trust (UK), with clinical data from electronic mental health and dementia records for patients across multiple clinical sites in South London harnessing the Clinical Record Interactive Search (CRIS) data platform from 2008-2022.

Findings to date

12,547 patients (age range 6–108, female 54%, male 46%) were identified with 14649 unique MR studies totalling 58620 MRI scans across 5 scanners with linked clinical data, from an ethnically diverse population (44% non-White), most with high social deprivation (n = 7424), and having a variety of diagnoses, of which F00-F09 Organic was the largest ICD category (n = 4628, 33% of total), followed by F20-F29 Schizophrenia and related disorders (n= 2151, 15% of total), and F30-F39 Mood [affective] disorders (n = 1607, 12% of total). The commonest recorded diagnoses were Alzheimer’s Disease (n = 2849), Schizophrenia (n = 1021) and mild cognitive impairment (n = 740).

Future Plans

SLaM Image Bank is an exceptionally rich real-world cohort with linked MRI and clinical data reflecting the diversity of the population of South London. It provides a unique platform for future testing of automated decision support tools that may identify unique signatures of diagnosis and neuropsychiatric symptom subtype, markers of prognosis, and provide stratification for interventions or trials that are potentially clinically meaningful. This will form the basis to which new linked data and MRI data will be added in order to grow this cohort alongside the potential additions of other imaging modalities, novel biomarkers, meta-data and patient groups in the future.

Strengths and limitations

SLaM Image Bank is a diverse large brain image cohort that grows with new scans linking MRI data to electronic mental health and dementia records from South London and Maudsley NHS Trust in London, UK, with data from 12,547 patients with a variety of psychiatric/neurological diagnoses over a 13-year period.

The aim is to provide clinically meaningful translation with an initial focus on identifying unique signatures of disease subtype and neuropsychiatric symptoms, markers of prognosis, and stratification for interventions or trials in a move towards real-world precision medicine alongside epidemiological and mechanistic research opportunities.

This is an imaging cohort that accrues from pre-existing data and does not require additional and expensive recruitment exercises which may in turn lead to a powerful, translational and scalable framework to build upon.