AIMC Topic: Aged
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Diagnosis of Thyroid Nodule Malignancy Using Peritumoral Region and Artificial Intelligence: Results of Hand-Crafted, Deep Radiomics Features and Radiologists' Assessment in Multicenter Cohorts.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Feb 15, 2025
OBJECTIVE: To develop, test, and externally validate a hybrid artificial intelligence (AI) model based on hand-crafted and deep radiomics features extracted from B-mode ultrasound images in differentiating benign and malignant thyroid nodules compare...
Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care.
Computer methods and programs in biomedicine
Feb 15, 2025
BACKGROUND AND OBJECTIVE: Scalable, flexible and highly interpretable tools for predicting mortality in residential aged care facilities for the purpose of informing and optimizing palliative care decisions, do not exist. This study is the first and ...
Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists.
European journal of radiology
Feb 15, 2025
PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines. Chest contrast-enhanced computed tomography (CECT) is crucial in the ...
Utilization of machine learning algorithm in the prediction of rehospitalization during one-year post traumatic spinal cord injury.
Spinal cord
Feb 15, 2025
STUDY DESIGN: Retrospective cohort study.
Enhanced heart failure mortality prediction through model-independent hybrid feature selection and explainable machine learning.
Journal of biomedical informatics
Feb 15, 2025
Heart failure (HF) remains a significant public health challenge with high mortality rates. Machine learning (ML) techniques offer a promising approach to predict HF mortality, potentially improving clinical outcomes. However, the effectiveness of th...
Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.
Cardiovascular diabetology
Feb 15, 2025
BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationsh...
Male
Middle Aged
Risk Assessment
Blood Glucose
Time Factors
Hospital Mortality
Hyperglycemia
Treatment Outcome
Predictive Value of Tests
Biomarkers
Retrospective Studies
Female
Risk Factors
Aged
Humans
Cause of Death
Machine Learning
Cardiac Surgical Procedures
Decision Support Techniques
Stress, Physiological
Databases, Factual
Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.
Cardiovascular diabetology
Feb 15, 2025
BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited.
Machine learning modeling for predicting adherence to physical activity guideline.
Scientific reports
Feb 15, 2025
This study aims to create predictive models for PA guidelines by using ML and examine the critical determinants influencing adherence to the PA guidelines. 11,638 entries from the National Health and Nutrition Examination Survey were analyzed. Variab...
Artificial intelligence can extract important features for diagnosing axillary lymph node metastasis in early breast cancer using contrast-enhanced ultrasonography.
Scientific reports
Feb 15, 2025
Contrast-enhanced ultrasound (CEUS) plays a pivotal role in the diagnosis of primary breast cancer and in axillary lymph node (ALN) metastasis. However, the imaging features that are clinically crucial for lymph node metastasis have not been fully el...
Deep learning-based organ-wise dosimetry of Cu-DOTA-rituximab through only one scanning.
Scientific reports
Feb 15, 2025
This study aimed to generate a delayed Cu-dotatate (DOTA)-rituximab positron emission tomography (PET) image from its early-scanned image by deep learning to mitigate the inconvenience and cost of estimating absorbed radiopharmaceutical doses. We acq...
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