AIMC Topic: Middle Aged

Clear Filters Showing 7051 to 7060 of 17155 articles

Clinical Application of Artificial Intelligence in the Ultrasound Classification of Hepatic Cystic Echinococcosis.

The American journal of tropical medicine and hygiene
Hepatic cystic echinococcosis (HCE) is a zoonotic disease that occurs when the larvae of Echinococcus granulosus parasitize the livers of humans and mammals. HCE has five subtypes, and accurate subtype classification is critical for choosing a treatm...

Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations.

Nutrients
This study undertakes a comprehensive examination of the intricate link between diet nutrition, age, and metabolic syndrome (MetS), utilizing advanced artificial intelligence methodologies. Data from the National Health and Nutrition Examination Surv...

Deep learning for risk stratification of thymoma pathological subtypes based on preoperative CT images.

BMC cancer
OBJECTIVES: This study aims to develop an innovative, deep model for thymoma risk stratification using preoperative CT images. Current algorithms predominantly focus on radiomic features or 2D deep features and require manual tumor segmentation by ra...

Deep-learning-based pelvic automatic segmentation in pelvic fractures.

Scientific reports
With the recent increase in traffic accidents, pelvic fractures are increasing, second only to skull fractures, in terms of mortality and risk of complications. Research is actively being conducted on the treatment of intra-abdominal bleeding, the pr...

Protocol for evaluating the fitness for purpose of an artificial intelligence product for radiology reporting in the BreastScreen New South Wales breast cancer screening programme.

BMJ open
INTRODUCTION: Radiologist shortages threaten the sustainability of breast cancer screening programmes. Artificial intelligence (AI) products that can interpret mammograms could mitigate this risk. While previous studies have suggested this technology...

The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study.

Journal of medical Internet research
BACKGROUND: Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of th...

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Frontiers in public health
INTRODUCTION: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to p...

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease.

Frontiers in endocrinology
INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve pati...

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.

Frontiers in immunology
INTRODUCTION: This study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scor...