AIMC Topic: Aged
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Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiography.
EBioMedicine
Jan 21, 2025
BACKGROUND: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for...
Development of an artificial intelligence system to indicate intraoperative findings of scarring in laparoscopic cholecystectomy for cholecystitis.
Surgical endoscopy
Jan 21, 2025
BACKGROUND: The surgical difficulty of laparoscopic cholecystectomy (LC) for acute cholecystitis (AC) and the risk of bile duct injury (BDI) depend on the degree of fibrosis and scarring caused by inflammation; therefore, understanding these intraope...
Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods.
Sensors (Basel, Switzerland)
Jan 21, 2025
In geriatric healthcare, missing data pose significant challenges, especially in systems used for frailty monitoring in elderly individuals. This study explores advanced imputation techniques used to enhance data quality and maintain model performanc...
AI Machine Learning-Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis.
JMIR formative research
Jan 21, 2025
BACKGROUND: Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population.
Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.
Cancer imaging : the official publication of the International Cancer Imaging Society
Jan 21, 2025
BACKGROUND: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significa...
Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.
Alzheimer's research & therapy
Jan 21, 2025
BACKGROUND: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (AD). Efficient screening for amyloid pathology is critical for identifying patients for early treatme...
Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients-model establishment, internal and external validation, and visualization.
Journal of translational medicine
Jan 21, 2025
OBJECTIVES: To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients.
Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.
BMC gastroenterology
Jan 21, 2025
OBJECTIVE: To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarteri...
Deep Learning
Liver Neoplasms
Carcinoma, Hepatocellular
Immune Checkpoint Inhibitors
Antineoplastic Combined Chemotherapy Protocols
Adult
Radiomics
Male
Middle Aged
Combined Modality Therapy
Chemoembolization, Therapeutic
Protein Kinase Inhibitors
Tyrosine Kinase Inhibitors
Humans
Prognosis
Retrospective Studies
Tomography, X-Ray Computed
Female
Aged
Machine learning based prediction models for the prognosis of COVID-19 patients with DKA.
Scientific reports
Jan 21, 2025
Patients with Diabetic ketoacidosis (DKA) have increased critical illness and mortality during coronavirus diseases 2019 (COVID-19). The aim of our study was to develop a predictive model for the occurrence of critical illness and mortality in COVID-...
Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.
Translational psychiatry
Jan 21, 2025
Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was s...
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