Primary Care

Obesity

Latest AI and machine learning research in obesity for healthcare professionals.

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Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by pro...

AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: An exploratory study.

BACKGROUND AND AIMS: Lifestyle intervention is the mainstay of therapy for metabolic dysfunction-ass...

Artificial Intelligence in Urology: Application of a Machine Learning Model to Predict the Risk of Urolithiasis in a General Population.

This research presents our application of artificial intelligence (AI) in predicting urolithiasis ri...

Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the bl...

Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data.

BACKGROUND: Dyslipidemia, characterized by variations in plasma lipid profiles, poses a global healt...

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review.

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intellig...

Integration of wearable devices and deep learning: New possibilities for health management and disease prevention.

In recent years, the market for wearable devices has been rapidly growing, with much of the demand f...

Supervised Machine Learning-Based Models for Predicting Raised Blood Sugar.

Raised blood sugar (hyperglycemia) is considered a strong indicator of prediabetes or diabetes melli...

The potential role for artificial intelligence in fracture risk prediction.

Osteoporotic fractures are a major health challenge in older adults. Despite the availability of saf...

Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010-2023).

Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high r...

Identifying the risk of exercises, recommended by an artificial intelligence for patients with musculoskeletal disorders.

Musculoskeletal disorders (MSDs) impact people globally, cause occupational illness and reduce produ...

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers.

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prev...

Integrative approach for efficient detection of kidney stones based on improved deep neural network architecture.

In today's digital world, with growing population and increasing pollution, unhealthy lifestyle habi...

Development of a machine learning-based risk model for postoperative complications of lung cancer surgery.

PURPOSE: To develop a comorbidity risk score specifically for lung resection surgeries.

Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery.

Postoperative ileus (POI) is a common complication after colorectal surgery, leading to increased h...

Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring.

Hypertension, a widespread cardiovascular issue, presents a major global health challenge. Tradition...

CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.

Screening high-risk populations is crucial for the prevention and treatment of kidney stones. Here, ...

A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning.

With the popularity of recreational activities, the study aimed to develop prediction models for re...

Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study.

OBJECTIVE: This study aimed to develop and evaluate machine-learning models for predicting the onset...

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