Latest AI and machine learning research in geriatrics for healthcare professionals.
Background: Traditional real-world studies comparing SGLT2 and DPP4 inhibitors on renal outcomes rely on propensity score matching, which causes high-dimensional data loss. We used causal machine learning (Causal ML) to unmask heterogeneous treatment effects in diabetic kidney disease (DKD). Methods: Using data from 4,588 patients within the Japanese J-CKD-DB-Ex registry, we implemented a doubly r...
Contrastive language-image learning (CLIP) has become a key paradigm for remote sensing vision-language understanding. However, existing remote sensing contrastive learning methods are mostly built on RGB-oriented CLIP architectures, making it difficult to exploit heterogeneous sensors such as SAR, multi-spectral imaging (MSI), and hyperspectral imaging (HSI). To address this limitation, we propos...
This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles. The platfo...
Precision oncology aims to tailor cancer treatment to tumor genetics but it currently benefits only a small fraction of patients, in part because the ...
Image enhancement and restoration have become standard back-end operations on short-video and social media platforms to boost UGC visual experience. Y...
Novel view synthesis from sparse inputs requires both geometric grounding from the observed views and generative priors of unobserved regions, motivat...
While unified multimodal models (UMMs) jointly perform visual understanding and generation within a single model, functional unification does not guar...
Background: The Centiloid (CL) scale standardizes global amyloid PET quantification and is widely used to define amyloid positivity. As a global summa...
We characterise the measurement error of a free- hand consumer structured-light scanner used to derive three- dimensional geometric wound descriptors....
Electroencephalography (EEG) is a promising, non-invasive, and cost-effective modality for Alzheimer's disease (AD) detection, but deep learning metho...
Agitation fluctuates over short time horizons in people living with dementia, yet continuous physiological information for anticipating next-day risk ...
Complex intracellular organization is a defining feature of eukaryotic cells, and the loss of its integrity is a hallmark of aging and disease. We com...
Individuals with Down syndrome (DS) display developmental delay, intellectual disability, premature brain ageing, and an increased risk of Alzheimer-l...
Constructing interpretable disease models from longitudinal omics data is a central challenge in precision medicine. The goal is a low-dimensional rep...
Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particularly for traditional linear methods that fail to c...
Neuropsychiatric symptoms (NPS) are increasingly recognized as critical components of the disease progression in Alzheimer's disease (AD), yet their r...
Early identification of cognitive impairment remains challenging in settings where comprehensive cognitive and clinical assessments are not available....
A plant-health score can appear precise while resting on duplicated image families, a long-tailed label space, or a runtime file that was never evalua...
Inorganic solid-state electrolytes must combine high room-temperature ionic conductivity, a wide electrochemical window, excellent electronic insulati...
Medical image foundation models can predict clinical phenotypes from computed tomography (CT), but strong performance leaves open whether they read di...