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Age Determination by Skeleton

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Classifying age from medial clavicle using a 30-year threshold: An image analysis based approach.

PloS one
This study aimed to develop image-analysis-based classification models for distinguishing individuals younger and older than 30 using the medial clavicle. We extracted 2D images of the medial clavicle from multi-slice computed tomography (MSCT) scans...

A machine learning approach for age prediction based on trigeminal landmarks.

Journal of forensic and legal medicine
OBJECTIVE: The aim of this study was to estimate the chronological age (CA) of a growing individual using a new machine learning approach on Cone Beam Computed Tomography (CBCT).

Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement.

Progress in orthodontics
OBJECTIVES: This study aimed to assess the accuracy of machine learning (ML) models with feature selection technique in classifying cervical vertebral maturation stages (CVMS). Consensus-based datasets were used for models training and evaluation for...

A critical comparative study of the performance of three AI-assisted programs for bone age determination.

European radiology
OBJECTIVES: To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, according to Greulich and Pyle (G&P). Therefore, the current study aimed to compare the performance of th...

Application of machine-learning methods in age-at-death estimation from 3D surface scans of the adult acetabulum.

Forensic science international
OBJECTIVE: Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate ind...

Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis.

BMC oral health
BACKGROUND: Artificial intelligence (AI) methods, including machine learning and deep learning, are increasingly applied in orthodontics for tasks like assessing skeletal maturity. Accurate timing of treatment is crucial, but traditional methods such...

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 ...

Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation.

International journal of legal medicine
Bone age estimation (BAE) is based on skeletal maturity and degenerative process of the skeleton. The clinical importance of BAE is in understanding the pediatric and growth-related disorders; whereas medicolegally it is important in determining crim...

An explainable machine learning estimated biological age based on morphological parameters of the spine.

GeroScience
Accurately estimating biological age is beneficial for measuring aging and predicting risk. It is widely accepted that the prevalence of spine compression increases significantly with age. However, biological age based on vertebral morphological data...