AIMC Topic: Obesity

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The use of machine learning in paediatric nutrition.

Current opinion in clinical nutrition and metabolic care
PURPOSE OF REVIEW: In recent years, there has been a burgeoning interest in using machine learning methods. This has been accompanied by an expansion in the availability and ease of use of machine learning tools and an increase in the number of large...

Robot-assisted vs laparoscopic bariatric procedures in super-obese patients: clinical and economic outcomes.

Journal of robotic surgery
The increased operative time and costs represent the main limitations of robotic technology application to bariatric surgery. Robotic platforms may help the surgeon to overcome the technical difficulties in super-obese (SO, BMI ≥ 50 kg/m) patients, i...

Development of a Non-Contact Sensor System for Converting 2D Images into 3D Body Data: A Deep Learning Approach to Monitor Obesity and Body Shape in Individuals in Their 20s and 30s.

Sensors (Basel, Switzerland)
This study demonstrates how to generate a three-dimensional (3D) body model through a small number of images and derive body values similar to the actual values using generated 3D body data. In this study, a 3D body model that can be used for body ty...

Impact of Visceral Fat Area on Intraoperative Complexity and Surgical Approach Decision for Robot-Assisted Partial Nephrectomy: A Comparative Analysis with BMI.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Optimizing surgical approaches for robot-assisted partial nephrectomy (RAPN) is vital for better patient outcomes. This retrospective study aimed to examine how visceral fat area (VFA) and body mass index (BMI) correlate with intraoperativ...

Reduced versus standard dose contrast volume for contrast-enhanced abdominal CT in overweight and obese patients using photon counting detector technology vs. second-generation dual-source energy integrating detector CT.

European journal of radiology
PURPOSE: To compare image quality of contrast-enhanced abdominal-CT using 1st-generation Dual Source Photon-Counting Detector CT (DS-PCD-CT) versus 2nd-generation Dual-Source Energy Integrating-Detector CT (DS-EID-CT) in patients with BMI ≥ 25, apply...

An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence.

PloS one
BACKGROUND: There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable...

Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

The British journal of radiology
OBJECTIVE: Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced d...

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning.

Frontiers in endocrinology
BACKGROUND: Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic un...

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques.

PloS one
AIM: To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them thr...

Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments.

International journal of obesity (2005)
BACKGROUND: The complex nature of obesity increasingly requires a comprehensive approach that includes the role of environmental factors. For understanding contextual determinants, the resources provided by technological advances could become a key f...