Geriatrics

Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data

Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring. Existing machine learning approaches have improved AD prediction using multimodal data, yet often focus on static classification or cohort-level risk estimation, providing limited support for subject-spe...

Principled Uncertainty in Clinical AI: End-to-End Bayesian Modelling and Algorithmic Equity Auditing Across Multimodal Patient Data

Clinical artificial intelligence (AI) systems routinely produce predictions without principled quantification of uncertainty, limiting their trustworthiness in high-stakes medical environments. This paper presents an integrated research programme addressing two interconnected problems: (1) the development of a fully end-to-end Bayesian uncertainty modelling framework for multimodal clinical data, ...

End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout

End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a form...

Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning

Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. T...

Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution

In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practi...

Multimodal sleep stage classification and label-free abnormality scoring in mid-to-older adults

Background: Sleep fragmentation and reduced sleep efficiency are markers of disrupted sleep architecture linked to cognitive and age-related decline. ...

MultiAge: A New Multidimensional Biomarker of Biological Age Derived from Comprehensive Phenotypic and Molecular Profiling

Background: It is an everyday observation that people of the same chronological age differ with respect to their physical and mental capacity. However...

Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach

The development of multilingual Alzheimer's Disease Dementia (AD) detection models presents significant challenges due to the resource-intensive and t...

LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video

Multimodal Large Language Models (MLLMs) have advanced image and video understanding and can increasingly handle longer visual inputs. Long-horizon ta...

Cross-scale spatially-aware generative modeling of transcriptomic programs underlying neurodegenerative brain organization

Neurodegenerative disorders such as Alzheimer's disease exhibit highly organized patterns of regional brain vulnerability, yet the biological mechanis...

From Pixels to Newtons: Predicting In Vivo Joint Contact Forces from Monocular Video

Joint contact forces govern implant longevity, cartilage health, and rehabilitation outcomes, shaping who develops osteoarthritis, who recovers well f...

Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws

Myocardial perfusion quantification using contrast-enhanced ultrasound offers a bedside non-ionizing alternative to nuclear imaging modalities. Howeve...

Prototyping a Generative AI-powered Person-centered Digital Health Tool to Mitigate Risk of Preventable Adverse Drug Events

Objectives: Older adults with comorbidities and polypharmacy have disproportionately high risk of hospitalization as well as readmission from adverse ...

Calibrating trust in AI-assisted pituitary surgery

Background: Endoscopic endonasal transsphenoidal surgery (EETS) requires navigation around neurocritical anatomy. Today, artificial intelligence clini...

Predicting the timing of first sustained cognitive worsening in Alzheimer's disease using real-world clinical data and machine learning

Background: Cognitive assessments are sparsely documented in electronic health records (EHRs), limiting scalable detection of cognitive worsening in r...

A Real-Time Automated Deep Learning Workflow for Non-invasive High-Magnification Imaging of C. elegans

Caenorhabditis elegans is a premier model organism for aging and neurobiology research, valued for its short lifespan, optical transparency, genetic t...

Medication-Wide Association Study of Alzheimer's Disease and Related Dementias: Identifying Drug Candidates from Electronic Health Records through Explainable AI

Objective: Alzheimer's disease (AD) is a leading cause of death and disability, and treatment options for Alzheimer's disease and related dementias (A...

Smart AI-Powered Machine Learning Risk Assessment for Early Osteoporosis Detection for Women Bone Health

Osteoporosis is often called a silent disease because it progresses without symptoms until a fracture occurs, posing a serious, yet frequently overloo...

Multiplex Proteomics of Lewy Body Dementia Reveals Cerebrospinal Fluid Biomarkers of Clinical and Neuropathological Heterogeneity

Lewy body dementia (LBD), which encompasses Parkinson's disease dementia (PDD) and Dementia with Lewy bodies (DLB), lacks established biofluid markers...

Trustworthy ML/AI for Aging Clocks: Preventing Systematic Prediction Bias in Biological Age Estimation

Machine learning (ML)- and artificial intelligence (AI)-based aging clocks are increasingly used to quantify physiological and molecular aging from om...