- Title
- Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings
- Creator
- Lee, Liz Yuanxi; Vaghari, Delshad; Chen, Christopher; Underwood, Benjamin R.; Rittman, Timothy; Kourtzi, Zoe; Burkhart, Michael C.; Tino, Peter; Montagnese, Marcella; Li, Zhuoyu; Zühlsdorff, Katharina; Giorgio, Joseph; Williams, Guy; Chong, Eddie
- Relation
- eClinicalMedicine Vol. 74, Issue August 2024, no. 102725
- Publisher Link
- http://dx.doi.org/10.1016/j.eclinm.2024.102725
- Publisher
- The Lancet Publishing Group
- Resource Type
- journal article
- Date
- 2024
- Description
- Background: Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic. Methods: We build a robust and interpretable predictive prognostic model (PPM) and validate its clinical utility using real-world, routinely-collected, non-invasive, and low-cost (cognitive tests, structural MRI) patient data. To enhance scalability and generalizability to the clinic, we: 1) train the PPM with clinically-relevant predictors (cognitive tests, grey matter atrophy) that are common across research and clinical cohorts, 2) test PPM predictions with independent multicenter real-world data from memory clinics across countries (UK, Singapore). Findings: PPM robustly predicts (accuracy: 81.66%, AUC: 0.84, sensitivity: 82.38%, specificity: 80.94%) whether patients at early disease stages (MCI) will remain stable or progress to Alzheimer's Disease (AD). PPM generalizes from research to real-world patient data across memory clinics and its predictions are validated against longitudinal clinical outcomes. PPM allows us to derive an individualized AI-guided multimodal marker (i.e. predictive prognostic index) that predicts progression to AD more precisely than standard clinical markers (grey matter atrophy, cognitive scores; PPM-derived marker: hazard ratio = 3.42, p = 0.01) or clinical diagnosis (PPM-derived marker: hazard ratio = 2.84, p < 0.01), reducing misdiagnosis. Interpretation: Our results provide evidence for a robust and explainable clinical AI-guided marker for early dementia prediction that is validated against longitudinal, multicenter patient data across countries, and has strong potential for adoption in clinical practice.
- Subject
- dementia prediction; prognosis; machine learnng; cognition; brain imaging
- Identifier
- http://hdl.handle.net/1959.13/1510372
- Identifier
- uon:56397
- Identifier
- ISSN:2589-5370
- Rights
- x
- Language
- eng
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