Obesity

AI and Chatbot-Supported Interventions for Physical Activity and Obesity-Related Lifestyle Behaviors: Scoping Review With Attention to Family Involvement.

BACKGROUND: AI-enabled chatbots and related conversational systems can facilitate human-computer interaction through natural language, personalization, and automated support. In pediatric health promotion, these tools have the potential to provide scalable and flexible approaches to support physical activity (PA) and related lifestyle behavior change within family contexts. However, evidence regar...

Development and validation of an interpretable machine learning model for predicting hypertension risk in patients with psoriasis.

OBJECTIVE: Hypertension is a common yet frequently underdiagnosed comorbidity in psoriasis patients. Early identification and blood pressure control are critical to improving outcomes. Although machine learning (ML) is widely used in disease prediction, a model for hypertension risk within the psoriasis population remains unavailable. This study aims to develop and validate such a model in patient...

Predictor Of Mortality In Obstructive Sleep Apnoea: Results of Explainable Deep Learning based Survival Analysis From a 15-Year Follow-Up.

INTRODUCTION: Obstructive sleep apnoea (OSA) is a respiratory disorder with comorbidities of several nature, from cardiovascular to renal ones. OSA is...

An explainable multi-label Diagnostic Prediction Model for lung diseases based on proteomic biomarkers.

OBJECTIVES: Differential diagnosis of pneumonia, tuberculosis, and lung cancer is highly challenging due to overlapping clinical presentations and hig...

AI Risk Prediction Tools for Autologous Breast Reconstruction.

BACKGROUND: Autologous breast reconstruction offers patients a durable and natural-appearing option after mastectomy. However, complication risks incl...

AI digital pathology as a key tool providing in-depth understanding of the progression and regression of MASH and fibrosis in male mouse models.

Optimizing murine models for studying Metabolic Dysfunction-Associated Steatohepatitis (MASH) and fibrosis is crucial for understanding disease mechan...

Automated deep learning detection of hepatic steatosis on non-contrast CT scans and discrepancy with scan reports.

BACKGROUND AND AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major cause of liver disease that is growing in prevalence....

Machine learning-based analysis of risk factors and construction of a predictive model for hyperuricemia in Chinese health examination population.

Hyperuricemia (HUA) imposes a growing public health burden, calling for better risk stratification tools. In this cross-sectional study of 4906 Chines...

Exploring the role of pain-related fear and lifting biomechanics in predicting low back pain incidence using supervised machine learning.

Greater lifting-specific pain-related fear has been associated with reduced lumbar spine motion during lifting, suggesting fear-driven protective move...

Targeting progressive multiple sclerosis: Toward mechanism-informed precision medicine.

Multiple sclerosis has undergone a therapeutic revolution over the past three decades. Randomized clinical trials and real-world data demonstrate that...

Post COVID-19 condition diagnosis among older people: findings from Swedish national register data.

INTRODUCTION: Post COVID-19 condition (PCC) denotes the persistence of symptoms following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)...

Prediction of factors contributing to Pain Intensity among low back pain patients: A comparative machine learning frameworks (Random Forest versus XGBoost).

BACKGROUND: Predicting pain intensity in patients with low back pain (LBP) remains a complex task due to the biopsychosocial nature of pain. Pain inte...

Bisphenol A drives the comorbidity of non-alcoholic fatty liver disease and osteoarthritis by targeting RHOB: Integrated multi-omics, single-cell analysis, and experimental validation.

BACKGROUND: This study focuses on exploring the co-morbid mechanisms by which Bisphenol A (BPA) induces non-alcoholic fatty liver disease (NAFLD) and ...

Bioinformatics, Machine Learning and Functional Validation Reveal Mitophagy-Related FABP5 and HMOX1 as Diagnostic and Therapeutic Targets in Polycystic Ovary Syndrome.

Polycystic Ovary Syndrome (PCOS) is a heterogeneous endocrine-metabolic disorder characterized by ovulatory dysfunction, hyperandrogenism, and insulin...

A clinically interpretable model for predicting pharyngocutaneous fistula after total laryngectomy.

BACKGROUND: Pharyngocutaneous fistula (PCF) is a frequent complication following total laryngectomy. While various risk models exist, their practical ...

A combi-elasto ultrasound based deep learning model on liver fibrosis staging for suspected MASLD/MASH patients.

OBJECTIVES: Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) weakened the ...

Mechanistic study on comorbidities of kidney stones and sepsis using transcriptome sequencing combined with single-cell spatial transcriptome sequencing.

Kidney stones and sepsis have a complex pathological association. Urinary obstruction and infection caused by kidney stones can easily induce sepsis, ...

Evaluating the quality, understandability, actionability, and readability of ChatGPT-generated patient information on overactive bladder: A cross-sectional study.

This cross-sectional study aimed to evaluate the quality, understandability, actionability, and readability of patient-oriented information generated ...

Low vitamin D status and cardiometabolic trait clustering in a primary-care adult cohort: association analysis and exploratory explainable machine-learning classification.

BACKGROUND: Low serum 25-hydroxyvitamin D (25(OH)D) commonly co-exists with obesity and adverse cardiometabolic traits. However, its cross-sectional a...

Interpretable machine-learning survival prediction of breast cancer prognosis from lifestyle factors: evidence from UK biobank.

BACKGROUND: Understanding how lifestyle habits influence breast cancer survival is crucial for improving long-term outcomes and guiding individualized...