AIMC Topic: Anthropometry
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Predictive value of anthropometric indices for incident of dyslipidemia: a large population-based study.
Population health metrics
Aug 19, 2025
INTRODUCTION: Dyslipidemia as a modifiable risk factor for chronic non-communicable diseases has become a worldwide concern. We aim to explore different anthropometric measures as predictors of dyslipidemia using various machine learning methods.
An AI-assisted tool for automated growth monitoring in pediatric achondroplasia.
European journal of pediatrics
Jul 18, 2025
UNLABELLED: Growth assessment in achondroplasia requires disorder-specific growth charts incorporating sex- and age-specific values. Manual calculations are tedious and subject to error. We present an artificial intelligence (AI)-assisted tool that a...
Machine learning models for predicting tibial intramedullary nail length.
BMC musculoskeletal disorders
Apr 21, 2025
BACKGROUND: Tibial intramedullary nailing (IMN) represents a standard treatment for fractures of the tibial shaft. Nevertheless, accurately predicting the appropriate nail length prior to surgery remains a challenging endeavour. Conventional techniqu...
Evaluation of Anthropometric Measurement Results and the Relationship Between Individual Identity and Geographic Belonging Through Artificial Neural Networks from a Mental Health Perspective.
Nigerian journal of clinical practice
Apr 11, 2025
BACKGROUND: Identity verification and geographical belonging are significant issues with mental health implications, particularly in forensic contexts. Anthropometric measurements offer potential insights into these relationships.
Development and validation of a machine learning model for predicting pediatric metabolic syndrome using anthropometric and bioelectrical impedance parameters.
International journal of obesity (2005)
Apr 1, 2025
OBJECTIVE: Metabolic syndrome (MS) is a risk factor for cardiovascular diseases, and its prevalence is increasing among children and adolescents. This study developed a machine learning model to predict MS using anthropometric and bioelectrical imped...
Predicting intra-abdominal hypertension using anthropometric measurements and machine learning.
Scientific reports
Mar 19, 2025
Almost one in four critically ill patients suffer from intra-abdominal hypertension (IAH). Currently, the gold standard for measuring intra-abdominal pressure (IAP) is via the bladder. Measurement of IAP is important to identify IAH early and thus im...
Artificial intelligence for weight estimation in paediatric emergency care.
BMJ paediatrics open
Mar 12, 2025
OBJECTIVE: To develop and validate a paediatric weight estimation model adapted to the characteristics of the Spanish population as an alternative to currently extended methods.
Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches.
BMC medical informatics and decision making
Jan 31, 2025
BACKGROUND: The aim of this study was to evaluate the potential models to determine the most important anthropometric factors associated with type 2 diabetes mellitus (T2DM).
3D face reconstruction for maxillofacial surgery based on morphable models and neural networks: A preliminary assessment for anthropometry accuracy.
Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Jan 28, 2025
OBJECTIVES: This study aimed to evaluate the anthropometric accuracy of 3D face reconstruction based on neural networks (3DFRBN) using 2D images, including the assessment of global errors and landmarks, as well as linear and angular measurements.
Prediction of the 10-year incidence of type 2 diabetes mellitus based on advanced anthropometric indices using machine learning methods in the Iranian population.
Diabetes research and clinical practice
Jun 25, 2024
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices.
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