Latest AI and machine learning research in geriatrics for healthcare professionals.
Introduction Postoperative complications after major surgery have substantial impacts on morbidity and resource utilisation. We investigated whether adding high-dimensional metabolomic and proteomic data to standard clinical variables would improve the prediction of a range of postoperative complications. Methods We analysed data from UK Biobank, a large prospective cohort study. Participants who ...
End-to-end In-Image Machine Translation (IIMT) aims to convert text embedded within an image into a target language while preserving the original visual context, layout, and rendering style. However, existing IIMT benchmarks are largely synthetic and thus fail to reflect real-world complexity, while current evaluation protocols focus on single-modality metrics and overlook cross-modal faithfulness...
Breast ultrasound diagnosis typically proceeds from global lesion localization to local sign assessment and then evidence integration to assign a BI-R...
Generative AI has advanced rapidly in medical report generation; however, its application to oral and maxillofacial CBCT reporting remains limited, la...
Identifying new therapeutic uses for existing drugs is a major challenge in biomedicine, especially for complex neurodegenerative conditions such as A...
We present an EEG-based approach to characterize disease-related spectro-temporal signatures in Alzheimer's disease (AD) and Parkinson's disease (PD)....
Osteoporosis is a skeletal disease typically diagnosed using dual-energy X-ray absorptiometry (DXA), which quantifies areal bone mineral density but o...
Recent progress in learning-based image compression has demonstrated that end-to-end optimization can substantially outperform traditional codecs by j...
Purpose: This study aimed to compare the reliability of myopia-related information from AI chatbots using a set of commonly asked questions by parents...
Osteoporosis is a skeletal disease typically diagnosed using dual-energy X-ray absorptiometry (DXA), which quantifies areal bone mineral density but o...
Document Image Machine Translation (DIMT) seeks to translate text embedded in document images from one language to another by jointly modeling both te...
Motion forecasting aims to predict the future trajectories of dynamic agents in the scene, enabling autonomous vehicles to effectively reason about sc...
Fetal ultrasound (US) is the primary imaging modality for prenatal screening, yet its interpretation relies heavily on the expertise of the clinician....
As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain l...
Motivation: Long-read metagenomic sequencing improves assembly contiguity and enables genome-resolved analysis of complex microbial communities, but a...
Unified multimodal models hold the promise of generating extensive, interleaved narratives, weaving text and imagery into coherent long-form stories. ...
Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although Multimod...
Artificial intelligence-based radiation therapy (RT) planning has the potential to reduce planning time and inter-planner variability, improving effic...
Large-scale biomedical vision-language models (VLMs) adapted on high-end imaging (e.g., CT) often fail to transfer to frontline low-end modalities (e....
Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarc...