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Obesity

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

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CardioAI: An Explainable Machine Learning System for Cardiovascular Risk Prediction and Patient Retention in Nigerian Healthcare Settings

Abstract Background: Cardiovascular disease is the leading cause of mortality in Nigeria and across sub-Saharan Africa, with rising incidence attributable to urbanisation, sedentary lifestyles, and limited access to early detection tools. Concurrently, patient dropout from rehabilitation programs remains a critical operational challenge for Nigerian clinics, with many patients failing to return af...

Comparison of United Kingdom (UK) and United States (U.S.) hypertension treatment status, physical activity and prospective mortality risk

Background: The 2017 American College of Cardiology/American Heart Association (ACC/AHA) guideline lowered diagnostic threshold for hypertension, encouraging earlier treatment initiation in the U.S. compared to UK, where the National Institute for Health and Care Excellence (NICE) guideline recommends higher thresholds. No comparative study evaluating how different hypertension guidelines and phys...

The Generative AI Meta-Evaluation (GAME) Study Framework: Global, Regional, and Country-Specific Unequal Difficulty of High BMI Intervention

Background High body mass index (BMI) presents a serious and ongoing global health challenge. However, the difficulty of high BMI intervention has not...

The Effects of AI-Guided Exercise and a Smart Ring on Arterial Stiffness (GONDOR-AS): protocol for a randomized controlled trial

Background: Cardiovascular disease (CVD) prevention is limited by the major challenge of low long-term adherence to effective lifestyle regimens. Arte...

A Novel Dual-Outcome Risk Calculator for Trial of Labor After Cesarean

Objective: To develop and validate a multivariable prediction model and clinically actionable risk score for vaginal birth after cesarean (VBAC) succe...

From Carb Counting to Diagnosis: Real World Patient Uses and Attitudes Toward Large Language Models in Diabetes Management

Managing diabetes-related conditions is time-intensive and cognitively demanding for patients and caregivers, requiring ongoing glucose monitoring, di...

Tracking the Changes in Longitudinal MRI-detected Perivascular Spaces following Ischaemic Stroke

Stroke is a leading cause of mortality and morbidity worldwide. MRI-visible perivascular spaces (PVS) are an emerging marker of cerebral small vessel ...

An Interpretable Machine Learning Framework for Non-Small Cell Lung Cancer Drug Response Analysis

Lung cancer is a condition where there is abnormal growth of malignant cells that spread in an uncontrollable fashion in the lungs. Some common treatm...

Mar 17 2026 2603.16330v1
Anatomy of aging through organ-resolved multi-modal imaging and deep learning

While aging manifests differently across organs and individuals, existing approaches to measure it lack the spatial resolution to capture this complex...

Genomic streamlining of seagrass-associated Colletotrichum sp. may be related to its adaptation to a marine monocot host

Colletotrichum spp. have a complicated history of association with land plants. Perhaps most well-known as plant pathogens for the devastating effect ...

A Rule-Based Machine Learning Model for Predicting Virological Failure Among Children Living With HIV in Malawi

Malawi's HIV treatment monitoring system faces serious challenges because of a shortage of experts and reliance on viral load testing every 3 to 12 mo...

Impact of Image Bit Depth Reduction on Deep Learning Performance in Chest Radiograph Analysis: A Multi-institutional Study

Purpose Medical imaging typically generates 12- to 16-bit formats, yet conversion to 8-bit is often required. While deep learning has been widely expl...

Predictors of COVID-19 hospital outcomes: a machine learning analysis of the National COVID Cohort Collaborative

Predicting hospital outcomes for patients with severe acute respiratory infections is critical for risk stratification and resource planning, yet hete...

Echocardiography-Based, Artificial Intelligence-Enabled Electrocardiography (AI-ECG) for Diastolic Hemodynamics Phenotyping in Acute Heart Failure (AHF)

Background: Acute heart failure (AHF) exhibits marked heterogeneity in diastolic hemodynamics, yet comprehensive echocardiographic assessment of diast...

Enhancing Prediabetes Diagnosis from Continuous Glucose Monitoring Data via Iterative Label Cleaning and Deep Learning

As of early 2026, over 115 million US adults (more than 1 in 3) have prediabetes, a condition with an annual conversion rate of 5%-10% to type 2 diabe...

Trustworthy personalized treatment selection: causal effect-trees and calibration in perioperative medicine

Background Personalized medicine promises to tailor treatments to the individual, but it carries a hidden risk: mistaking statistical noise for action...

Enhanced Insights into Alcohol Use Disorder from Lifestyle, Background, and Family History in a Large-Scale Machine Learning Study

Alcohol Use Disorder (AUD) is a multifactorial condition with severe individual and societal impacts. Extending our 2024 study, this work examines lif...

Multi-Omics Integration of Transcriptomics and Metabolomics with Machine Learning Uncovers Novel Risk Factors for Alzheimer's disease

Background: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deteriorati...

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