AIMC Topic: Age Determination by Skeleton

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Automated Bone Age Assessment and Adult Height Prediction from Pediatric Hand Radiographs via a Cascaded Deep Learning Framework.

Journal of medical systems
Bone age assessment and adult height prediction are essential for evaluating pediatric growth. Traditional methods rely on manual radiographic interpretation, which is subjective, time-consuming, and prone to inter-observer variability. This study pr...

Age estimation of children and adolescents from mandibles using machine learning.

Scientific reports
Age estimation is a crucial step in forensic identification, particularly in scenarios where dental structures may be absent. This study aimed to develop and evaluate supervised machine learning models to predict chronological age based on mandibular...

Research hotspots and trends of pediatric bone age: A bibliometric and visualization analysis.

Lasers in medical science
PURPOSE: Research related to pediatric bone age has gained substantial scholarly attention over recent decades, given its critical importance in monitoring growth and guiding clinical decision-making in children. This study aims to identify research ...

Population-specific calibration and validation of an open-source bone age AI.

Scientific reports
Assessing skeletal maturity through bone age (BA) evaluation is crucial for monitoring children's growth and guiding treatments, such as hormonal therapy and orthopedic interventions. In recent years, artificial intelligence (AI) methods have been de...

Determination of Skeletal Age From Hand Radiographs Using Deep Learning.

The American journal of sports medicine
BACKGROUND: Surgeons treating skeletally immature patients use skeletal age to determine appropriate surgical strategies. Traditional bone age estimation methods utilizing hand radiographs are time-consuming.

Comparison among artificial intelligence-based age estimation from morphological analysis of the pubic symphysis versus experienced and novice practitioners using a new atlas for component labeling.

International journal of legal medicine
Traditional age estimation methods based on macroscopic observation has been criticized for being excessively dependent on the observer's experience. The aim of this technical note is to propose a new atlas to assist the forensic practitioner in labe...

Brain tissue biomarker impact bone age in central precocious puberty more than hormones: a quantitative synthetic magnetic resonance study.

Japanese journal of radiology
OBJECTIVE: To investigate which brain tissue component volume (BTCV) biomarkers may be more effective than hormones in influencing bone age development in central precocious puberty (CPP).

Skull CT metadata for automatic bone age assessment by using three-dimensional deep learning framework.

International journal of legal medicine
Bone age assessment (BAA) means challenging tasks in forensic science especially in some extreme situations like only skulls found. This study aimed to develop an accurate three-dimensional deep learning (DL) framework at skull CT metadata for BAA an...

Deep learning based quantitative cervical vertebral maturation analysis.

Head & face medicine
OBJECTIVES: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives ...

The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.

International journal of legal medicine
INTRODUCTION: Age estimation, especially in adults, presents substantial challenges in different contexts ranging from forensic to clinical applications. Bone mineral density (BMD), with its distinct age-related variations, has emerged as a critical ...