Machine learning-based prediction of future dementia using routine clinical MRI brain scans and healthcare data

ImportanceEarly identification of dementia risk is essential for preventive care and timely enrolment into disease-modifying interventions. Current approaches rely on costly, invasive, or research-only methods not feasible at scale within public health systems. ObjectiveTo test whether routinely acquired NHS brain MRI scans can be used to predict future dementia diagnosis and whether confidence-based stratification improves prediction reliability and clinical interpretability. Design, Setting, and ParticipantsRetrospective case-control study conducted entirely within a secure NHS Trusted Research Environment. Routine T1-weighted MRI brain scans were linked with electronic health records for participants from Tayside and Fife, Scotland. The study included 259 individuals who subsequently developed dementia and 259 age- and sex-matched controls. Data were processed and modelled between January and June 2025. ExposureMRI-derived structural brain features analysed using a support-vector-machine model with nested cross-validation and distance-from-hyperplane (DFH) confidence calibration. Main Outcomes and MeasuresPrimary outcomes were prediction accuracy and area under the receiver-operating-characteristic curve (AUC). Secondary analyses assessed DFH-stratified performance and time from scan to clinical diagnosis. ResultsConfidence-based filtering identified a high-confidence subgroup ({approx}35% of scans) with {approx}80% accuracy. Overall, the model predicted future dementia up to five years before first recorded NHS diagnosis, achieving 66.8% accuracy (AUC = 0.71). Model sensitivity increased for shorter time-to-diagnosis intervals. Analyses were generalisable across heterogeneous routine NHS scanners and datasets. Conclusions and RelevanceThis study provides, to our knowledge, the first demonstration that routinely collected NHS MRI data can predict future dementia years before clinical diagnosis. Incorporating confidence calibration transforms a standard classifier into a safety-aware, clinically interpretable framework, supporting scalable early detection, risk stratification, and recruitment to preventive or disease-modifying trials across population health systems. Key PointsO_ST_ABSQuestionC_ST_ABSCan routinely acquired NHS magnetic resonance imaging (MRI) brain scans and linked health records be used to predict future dementia, with confidence calibration suitable for clinical and population-level use? FindingsIn this retrospective case-control study of 518 individuals, a support-vector-machine model trained on routine NHS MRI data predicted future dementia up to five years before diagnosis. Confidence-based stratification identified a high-confidence subgroup ([~]35% of scans) with [~]80% accuracy. MeaningRoutine NHS imaging data can be transformed into a population-scale, privacy-preserving early detection framework, where confidence calibration allows safe, clinically interpretable application for preventive care and research recruitment.