Obesity

Artificial intelligence in pediatric endocrinology: clinical applications, governance, and future directions.

PURPOSE OF REVIEW: Artificial intelligence has transitioned from theoretical promise to practical implementation across medicine including pediatric endocrinology. This review examines artificial intelligence applications across diabetes care, obesity management, thyroid disorders, growth and puberty, and bone age assessment followed by discussion of limitations and future directions. RECENT FINDI...

Predicting depression treatment outcomes for cognitive behavioural therapy using machine learning: A systematic review and meta-analysis.

BACKGROUND: Cognitive behavioural therapy (CBT) is an empirically-supported treatment for depression, although some patients respond well and others do not. The use of machine learning (ML) could potentially help to predict which patients may benefit most from CBT. OBJECTIVES: To synthesise the results of CBT studies applying ML to predict depression treatment outcomes. METHODS: Systematic searche...

BD-KDD: A real-world clinical dataset for kidney disease diagnosis and healthy classification.

Kidney disease is a major global health concern that requires timely diagnosis and effective monitoring to prevent severe complications and improve pa...

A Wavelet AI Algorithm to Automatically Identify Postprandial Glucose Responses From Continuous Glucose Monitoring Profiles in People Without Diabetes.

BACKGROUND: Automated identification of postprandial glucose responses (PPGR) from continuous glucose monitoring (CGM) profiles may detect early dysgl...

Cardioprotection in oncology: Mechanisms, risks and therapeutic strategies.

Objectives of this review is to conduct an analysis of current data of cardiotoxicity of anticancer drugs and radiation therapy, methods of cardiovasc...

Machine and Deep Learning Models for Preoperative Prediction of Suboptimal Clinical Response One Year after Metabolic Bariatric Surgery: Findings from the Tehran Obesity Treatment Study.

Association between triglyceride-glucose index and related indicators with heart failure-related mortality in individuals with early-stage cardiovascular-kidney-metabolic syndrome.

BACKGROUND: Insulin resistance is a central pathophysiological feature of cardiovascular-kidney-metabolic (CKM) syndrome and has been implicated in ad...

AI-driven appendicular skeletal muscle mass index (ASMI) prediction and low muscle mass detection from routine hip X-rays: a novel opportunistic case finding tool.

PURPOSE: Sarcopenia diagnosis requires identifying low muscle mass (LMM), typically via dual-energy X-ray absorptiometry (DXA). However, DXA's limited...

Circulatory dietary and gut-derived metabolites predict early cognitive decline.

INTRODUCTION: A key component of disease prevention is the identification of at-risk individuals. Microbial dysbiosis in the early stages of cognitive...

Multi-omics and machine learning reveal a critical mediator of PRKCA in quercetin-mediated protection against Sarcopenia.

BACKGROUND: Sarcopenia, the age-associated loss of skeletal muscle mass and function, poses a growing public health challenge. Although dietary flavon...

[Construction of a prognostic assessment model of acupuncture intervention for lumbar disc herniation based on interpretable machine learning algorithms].

OBJECTIVE: To construct and validate a prognostic assessment model of acupuncture intervention for lumbar disc herniation (LDH), and analyze the key f...

Improving support and self-management of ophthalmic patients using an artificial intelligence health coach.

This study evaluates patient engagement and satisfaction with Everyday Medical Monitoring Ally (E.M.M.A), a purpose-trained artificial intelligence (A...

Machine learning predicts hepatocellular carcinoma risk from routine clinical data: a large population-based multicentric study.

Hepatocellular carcinoma (HCC) is a highly fatal tumor, for which risk stratification is crucial, yet remains challenging. Here, we develop an interpr...

SEMASEARCH Study Design: Real-World Evaluation of Semaglutide 2.4 mg in Adults With Severe Obesity Underrepresented in Clinical Trials.

BACKGROUND: Although semaglutide 2.4 mg has demonstrated significant weight loss efficacy in clinical trials, real-world data, particularly with regar...

FDS-CAP: Modeling Fragmented Disease Subgraphs with Component-Level Attention for Comorbidity Prediction.

Understanding comorbidity between human diseases is essential for uncovering shared pathophysiological mechanisms and improving diagnostic and therape...

Perioperative blood loss in cardiac surgery: Validation of machine learning-derived clusters.

BackgroundThere is no universally accepted definition of perioperative blood loss in cardiac surgery. Existing methods are based on chest tube output ...

Multi-omics integration reveals ANXA6-high γδ T cell-endothelial communication as a potential link between periodontitis and MASLD.

Periodontitis (PD) and metabolic dysfunction-associated steatotic liver disease (MASLD) were highly prevalent inflammatory disorders that frequently c...

Wearable devices and cardiovascular health: revolutionizing remote monitoring and disease prevention.

Wearable devices are transforming cardiovascular medicine by enabling continuous monitoring of physiologic and behavioural measures outside of traditi...

An interpretable machine learning model combining MRI-DKI habitat radiomic features and clinical biomarkers for noninvasive prediction of lymphatic metastasis in rectal cancer: a prospective study.

OBJECTIVE: Tumor heterogeneity exerts a significant influence on lymphovascular invasion (LVI) and lymph node metastasis (LNM) in rectal cancer (RC), ...

Using machine learning to predict the small for gestational age and identify the important predictors: A real-world clinical cohort study in China.

PURPOSE: Aims to use machine learning to predict the risk of small for gestational age (SGA) and identify its important predictors. METHODS: This is a...