Latest AI and machine learning research in geriatrics for healthcare professionals.
The automated evaluation of cognitive status utilizing multimedia technologies presents a promising frontier in early dementia diagnosis. However, the development of robust machine learning models for cognitive impairment detection is frequently hindered by the scarcity of large-scale, strictly synchronized, and clinically validated multimodal datasets. To bridge this critical gap, we introduce th...
This paper proposes an end-to-end shared attention estimation method via group detection. Most previous methods estimate shared attention (SA) without detecting the actual group of people focusing on it, or assume that there is a single SA point in a given image. These issues limit the applicability of SA detection in practice and impact performance. To address them, we propose to simultaneously a...
Objective: Behavioral and social factors (BSFs) substantially influence the risk, onset, and progression of Alzheimer disease and related dementias (A...
Biological staging models are a key tool for assessing the severity of Alzheimer's disease (AD), supporting personalized medicine and playing a critic...
Precision neuroimaging aims to deliver individualized assessments of brain health, yet a single structural MRI does not yield a multidimensional, quan...
Protein language models (PLMs) are increasingly central to protein engineering and drug discovery. Many high-performing systems, however, rely on larg...
The aging clock paradigm has yielded dozens of specialist models that can estimate chronological age or mortality from virtually any biodata type. Yet...
Biological brain aging is a major determinant of cognitive decline and neurodegenerative disease, yet scalable and intervention-ready brain aging biom...
Optical character recognition remains critical infrastructure for document digitization, yet state-of-the-art performance is often restricted to well-...
Driving video generation has achieved much progress in controllability, video resolution, and length, but fails to support fine-grained object-level c...
Low left ventricular ejection fraction (LEF) frequently remains undetected until progression to symptomatic heart failure, underscoring the need for s...
Automatically extracting chemical structures from documents is essential for the large-scale analysis of the literature in chemistry. Automatic pipeli...
Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegen...
Medical Visual Question Answering (MedVQA) models often exhibit limited generalization due to reliance on dataset-specific correlations, such as recur...
Multimodal Large Language Models (MLLMs) achieve strong multimodal reasoning performance, yet we identify a recurring failure mode in long-form genera...
Longitudinal electronic health records (EHRs) form irregular event sequences that mix multiple clinical coding systems and care settings. Learning tra...
We present Feature-Align CNN (FA-CNN), a prototype CNN architecture with intrinsic class attribution through end-to-end feature alignment. Our intuiti...
Resting-state electroencephalography (rs-EEG) offers a cost effective and portable alternative to conventional neuroimaging for dementia screening, ye...
Background: Experiences of violence are reported frequently by mental health service users, victims of violence are at a greater risk of mental health...
Cardiovascular diseases remain the leading cause of global mortality, and early risk stratification is critical for improving prognosis. Artificial in...