AIMC Topic: Aged, 80 and over
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A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer's Disease With Fusion Network Using Eye Movements and Speech.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 22, 2025
Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to the overlap of initial symptoms with normal aging processes. Given that no cure exists and current ...
Using the Geriatric Emergency Perioperative Risk Index Derived From Artificial Intelligence Algorithms to Predict Outcomes of Geriatric Emergency General Surgery.
The Journal of surgical research
Apr 21, 2025
INTRODUCTION: The objective of this study was to employ artificial intelligence (AI) technology for the development of a model that can accurately forecast the outcome of emergency general surgery (EGS) in elderly patients. Additionally, an innovativ...
Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections.
Radiation oncology (London, England)
Apr 18, 2025
BACKGROUND: Delineating the internal gross tumor volume (IGTV) is crucial for the treatment of non-small cell lung cancer (NSCLC). Deep learning (DL) enables the automation of this process; however, current studies focus mainly on multiple phases of ...
Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation.
JMIR medical informatics
Apr 18, 2025
BACKGROUND: Delirium is common in hospitalized patients and is correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screeni...
Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.
Epilepsy & behavior : E&B
Apr 17, 2025
BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, whether AD and epilepsy share pathophysiological changes remains poorly defined. In this study, we levera...
Predicting depression and unravelling its heterogeneous influences in middle-aged and older people populations: a machine learning approach.
BMC psychology
Apr 17, 2025
BACKGROUND: Aging has become a global trend, and depression, as an accompanying issue, poses a significant threat to the health of middle-aged and older adults. Existing studies primarily rely on statistical methods such as logistic regression for sm...
Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.
BMC musculoskeletal disorders
Apr 17, 2025
RATIONALE AND OBJECTIVES: To establish an automated osteoporosis detection model based on low-dose abdominal CT (LDCT). This model combined a deep learning-based automatic segmentation of the proximal femur with a radiomics-based bone status classifi...
Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.
Frontiers in cellular and infection microbiology
Apr 17, 2025
BACKGROUND: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to the symptom similarity with other neurological disorders and the absence of specific biomarkers, ear...
Quantitative Ischemic Lesions of Portable Low-Field Strength MRI Using Deep Learning-Based Super-Resolution.
Stroke
Apr 16, 2025
BACKGROUND: Deep learning-based synthetic super-resolution magnetic resonance imaging (SynthMRI) may improve the quantitative lesion performance of portable low-field strength magnetic resonance imaging (LF-MRI). The aim of this study is to evaluate ...
Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.
International journal of neural systems
Apr 16, 2025
Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated with adverse outcomes such as postoperative delirium, extended recovery time, and increased postope...
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