AIMC Topic: Intra-Abdominal Fat

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METS-VF as a novel predictor of gallstones in U.S. adults: a cross-sectional analysis (NHANES 2017-2020).

BMC gastroenterology Jul 31, 2025
BACKGROUND AND AIMS: Obesity is a well-established risk factor for gallstone formation, but traditional anthropometric measures (e.g., BMI, waist circumference) inadequately assess metabolically active visceral adiposity. The novel Metabolic Score fo...
Aged Aged, 80 and over Young Adult Adult Cross-Sectional Studies Humans Female Risk Factors Intra-Abdominal Fat Gallstones United States Metabolic Syndrome Obesity Predictive Value of Tests Male Waist Circumference Middle Aged Prevalence ROC Curve Nutrition Surveys
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Deep learning models for deriving optimised measures of fat and muscle mass from MRI.

Scientific reports Jul 17, 2025
Fat and muscle mass are potential biomarkers of wellbeing and disease in oncology, but clinical measurement methods vary considerably. Here we evaluate the accuracy, precision and ability to track change for multiple deep learning (DL) models that qu...
Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Muscle, Skeletal Adipose Tissue Female Reproducibility of Results Adult Male Middle Aged Intra-Abdominal Fat Subcutaneous Fat Humans Neural Networks, Computer
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Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES.

BMC medical informatics and decision making Jul 1, 2025
OBJECTIVE: Using 2005-2018 NHANES data, this study examined the association between the visceral fat metabolism score (METS-VF) and heart failure (HF) prevalence in U.S. adults, leveraging machine learning (LightGBM/XGBoost) and SHAP for classficatio...
Adult Male Middle Aged Nutrition Surveys United States Heart Failure Intra-Abdominal Fat Boosting Machine Learning Algorithms Humans Machine Learning Female Aged
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Evaluation of semi-automated versus fully automated technologies for computed tomography scalable body composition analyses in patients with severe acute respiratory syndrome Coronavirus-2.

Clinical nutrition ESPEN Jun 11, 2025
RATIONALE AND OBJECTIVES: Fully automated, artificial intelligence (AI) -based software has recently become available for scalable body composition analysis. Prior to broad application in the clinical arena, validation studies are needed. Our goal wa...
Adipose Tissue Body Composition Intra-Abdominal Fat Aged Adult Humans Male Artificial Intelligence Middle Aged Software Muscle, Skeletal COVID-19 Retrospective Studies Tomography, X-Ray Computed Female SARS-CoV-2
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Effect of visceral fat on onset of metabolic syndrome.

Scientific reports May 30, 2025
This study analysed the effects of visceral fat on metabolic syndrome (MetS) and developed an algorithm to predict its onset using health examination data from the Iwaki Health Promotion Project in Japan. The dataset included 213 cases of MetS onset ...
Humans Intra-Abdominal Fat Male Machine Learning Middle Aged Algorithms Metabolic Syndrome Female Japan Aged Adult
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Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies.

Scientific reports Apr 7, 2025
Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessi...
Male Middle Aged Deep Learning Image Processing, Computer-Assisted Sarcopenia Humans Tomography, X-Ray Computed Female Aged Aged, 80 and over Muscle, Skeletal Body Composition Psoas Muscles Intra-Abdominal Fat Subcutaneous Fat
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Measurement of adipose body composition using an artificial intelligence-based CT Protocol and its association with severe acute pancreatitis in hospitalized patients.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver Mar 21, 2025
BACKGROUND/OBJECTIVES: The clinical utility of body composition in predicting the severity of acute pancreatitis (AP) remains unclear. We aimed to measure body composition using artificial intelligence (AI) to predict severe AP in hospitalized patien...
Intra-Abdominal Fat Pancreatitis Humans Middle Aged Hospitalization Artificial Intelligence Severity of Illness Index Aged Retrospective Studies Adipose Tissue Adult Tomography, X-Ray Computed Acute Disease Male Female Body Composition
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Multimodal deep learning: tumor and visceral fat impact on colorectal cancer occult peritoneal metastasis.

European radiology Feb 17, 2025
OBJECTIVES: This study proposes a multimodal deep learning (DL) approach to investigate the impact of tumors and visceral fat on occult peritoneal metastasis in colorectal cancer (CRC) patients.
Humans Retrospective Studies Male Middle Aged Deep Learning Peritoneal Neoplasms Intra-Abdominal Fat Tomography, X-Ray Computed Female Aged Adult Colorectal Neoplasms
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Preoperative blood and CT-image nutritional indicators in short-term outcomes and machine learning survival framework of intrahepatic cholangiocarcinoma.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Jan 30, 2025
BACKGROUND&AIMS: Intrahepatic cholangiocarcinoma (iCCA) is aggressive with limited treatment and poor prognosis. Preoperative nutritional status assessment is crucial for predicting outcomes in patients. This study aimed to compare the predictive cap...
Humans Machine Learning Prognosis Retrospective Studies Tomography, X-Ray Computed Female Aged Survival Rate Intra-Abdominal Fat Bilirubin Male Serum Albumin Hepatectomy Middle Aged Cholangiocarcinoma Bile Duct Neoplasms Nutrition Assessment Subcutaneous Fat Nutritional Status Postoperative Complications Preoperative Period Neoplasm Recurrence, Local
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Artificial intelligence-based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus.

Journal of diabetes investigation Nov 22, 2024
AIM/INTRODUCTION: We assess the efficacy of artificial intelligence (AI)-based, fully automated, volumetric body composition metrics in predicting the risk of diabetes.
Cross-Sectional Studies Tomography, X-Ray Computed Humans Diabetes Mellitus Male Female Longitudinal Studies Artificial Intelligence Middle Aged Risk Factors Prognosis Follow-Up Studies Prevalence Aged Retrospective Studies Incidence Body Composition Adult Intra-Abdominal Fat
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