AIMC Topic: Female

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New Method of Early RRMS Diagnosis Using OCT-Assessed Structural Retinal Data and Explainable Artificial Intelligence.

Translational vision science & technology Feb 3, 2025
PURPOSE: The purpose of this study was to provide the development of a method to classify optical coherence tomography (OCT)-assessed retinal data in the context of automatic diagnosis of early-stage multiple sclerosis (MS) with decision explanation.
Female Young Adult Adult Male Humans Artificial Intelligence Early Diagnosis Nerve Fibers Multiple Sclerosis, Relapsing-Remitting Middle Aged Support Vector Machine Tomography, Optical Coherence Retina
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Deep Learning Approaches to Predict Geographic Atrophy Progression Using Three-Dimensional OCT Imaging.

Translational vision science & technology Feb 3, 2025
PURPOSE: To evaluate the performance of various approaches of processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate of geographic atrophy (GA) lesions caused by ...
Humans Macular Degeneration Disease Progression Geographic Atrophy Deep Learning Neural Networks, Computer Imaging, Three-Dimensional Female Tomography, Optical Coherence Aged Male
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Advanced Artificial-Intelligence-Based Jiang Formula for Intraocular Lens Power in Congenital Ectopia Lentis.

Translational vision science & technology Feb 3, 2025
PURPOSE: The purpose of this study was to develop an artificial intelligence (AI)-based intraocular lens (IOLs) power calculation formula for improving the accuracy of IOLs power calculations in patients with congenital ectopia lentis (CEL).
Visual Acuity Optics and Photonics Lens Implantation, Intraocular Ectopia Lentis Refraction, Ocular Humans Lenses, Intraocular Artificial Intelligence Adolescent Female Child Young Adult Adult Male
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Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels.

Clinical chemistry Feb 3, 2025
BACKGROUND: Intravenous (IV) fluid contamination within clinical specimens causes an operational burden on the laboratory when detected, and potential patient harm when undetected. Even mild contamination is often sufficient to meaningfully alter res...
Female Sensitivity and Specificity Male Humans Ensemble Learning Machine Learning Algorithms Prospective Studies
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[Establishing of mortality predictive model for elderly critically ill patients using simple bedside indicators and interpretable machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue Feb 1, 2025
OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assess...
Female Prognosis APACHE Humans Organ Dysfunction Scores Machine Learning Aged Retrospective Studies Algorithms Critical Illness Male Intensive Care Units Hospital Mortality
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Constructing a Prognostic Model for Subtypes of Colorectal Cancer Based on Machine Learning and Immune Infiltration-Related Genes.

Journal of cellular and molecular medicine Feb 1, 2025
This study constructed a prognostic model combining machine learning-based immune infiltration-related genes in each CRC subtype. We used publicly accessible gene expression data and clinical information on colorectal cancer patients. Integrated bioi...
Biomarkers, Tumor Male Humans Gene Expression Profiling Gene Regulatory Networks Machine Learning Algorithms ROC Curve Computational Biology Prognosis Gene Expression Regulation, Neoplastic Female DNA Copy Number Variations Colorectal Neoplasms
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A systematic exposure-wide framework leveraging machine learning to identify multidomain exposure factors and their joint influence on cognitive function: Evidence from a neurological cohort.

Alzheimer's & dementia : the journal of the Alzheimer's Association Feb 1, 2025
INTRODUCTION: Cognitive decline has become a growing public concern, yet large-scale exposure data identifying the contributing factors remain limited.
Life Style Neuropsychological Tests Cognitive Dysfunction Cohort Studies Cognition Aged Humans Cluster Analysis Male Machine Learning Middle Aged Female Risk Factors
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Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.

Journal of cachexia, sarcopenia and muscle Feb 1, 2025
BACKGROUND: Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional care quality. Real-time location system (RTLS) enables continuous tracking of patient location, pro...
Humans Machine Learning Retrospective Studies Electronic Health Records Republic of Korea Female Accidental Falls Male Risk Factors Middle Aged Hospitalization Risk Assessment Aged Hospitals Aged, 80 and over
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Impact of Hydroxy-Methyl-Butyrate Supplementation on Malnourished Patients Assessed Using AI-Enhanced Ultrasound Imaging.

Journal of cachexia, sarcopenia and muscle Feb 1, 2025
BACKGROUND: This study aimed to evaluate the effects of an oral nutritional supplement (ONS) enriched with hydroxy-methyl-butyrate (HMB) in subjects with disease-related malnutrition (DRM) and to monitor these effects with an ultrasound Imaging Syste...
Sarcopenia Malnutrition Valerates Nutrition Assessment Ultrasonography Dietary Supplements Humans Artificial Intelligence Female Aged Adult Male Middle Aged
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Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data.

Alzheimer's & dementia : the journal of the Alzheimer's Association Feb 1, 2025
INTRODUCTION: Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers.
Positron-Emission Tomography tau Proteins Machine Learning Biomarkers Brain Female Aged Male Magnetic Resonance Imaging Alzheimer Disease Humans
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