AI Medical Compendium Journal:
Obesity (Silver Spring, Md.)

Showing 1 to 5 of 5 articles

Transforming Big Data into AI-ready data for nutrition and obesity research.

Obesity (Silver Spring, Md.)
OBJECTIVE: Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), h...

Increased brain fractional perfusion in obesity using intravoxel incoherent motion (IVIM) MRI metrics.

Obesity (Silver Spring, Md.)
OBJECTIVE: This research seeks to shed light on the associations between brain perfusion, cognitive function, and mental health in individuals with and without obesity.

Gender-specific data-driven adiposity subtypes using deep-learning-based abdominal CT segmentation.

Obesity (Silver Spring, Md.)
OBJECTIVE: The aim of this study was to quantify abdominal adiposity and generate data-driven adiposity subtypes with different diabetes risks.

Low Circulating Levels of Neurotensin in Women with Nonalcoholic Fatty Liver Disease Associated with Severe Obesity.

Obesity (Silver Spring, Md.)
OBJECTIVE: This study was performed to investigate neurotensin plasma levels in patients with nonalcoholic fatty liver disease (NAFLD) associated with severe obesity.

Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns.

Obesity (Silver Spring, Md.)
OBJECTIVE: Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct ca...