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Age Estimation Using Machine Learning Algorithms with Parameters Obtained from X-ray Images of the Calcaneus.

R Ciftci¹, Y Secgin², Z Oner³

¹Department of Anatomy, Faculty of Medicine, Gaziantep Islam Science and Technology University, Gaziantep, Türkiye.

Nigerian Journal of Clinical Practice

|February 26, 2024

Summary

This summary is machine-generated.

Machine learning models using calcaneus (heel bone) X-ray measurements can accurately estimate bone age. The Extra Tree Classifier achieved 0.85 accuracy, highlighting the calcaneus

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Area of Science:

Forensic science
Radiology
Biomedical engineering

Background:

Bone age determination is crucial for forensic, surgical, and scientific applications.
Accurate age estimation aids in legal investigations, medical diagnoses, and anthropological studies.

Purpose of the Study:

To estimate age with high accuracy and precision using Machine Learning (ML) algorithms.
To analyze parameters derived from calcaneus (heel bone) X-ray images of healthy individuals.

Main Methods:

Retrospective analysis of foot X-ray images from 341 individuals aged 18-65 years.
Measurement of calcaneus parameters: maximum width (MW), body width (BW), maximum length (MAXL), minimum length (MINL), facies articularis cuboidea height (FACH), maximum height (MAXH), and tuber calcanei width (TKW).
Application of ML models to age estimation using grouped measurements (20-45, 46-64, 65+ years).

Main Results:

The Extra Tree Classifier algorithm achieved an accuracy of 0.85 in age estimation.
Other ML algorithms demonstrated accuracy rates ranging from 0.78 to 0.82.
The maximum height (MAXH) parameter of the calcaneus showed the highest contribution to age estimation, as determined by SHAP analysis.

Conclusions:

The calcaneus bone provides a reliable basis for accurate and precise age estimations.
ML algorithms applied to calcaneus measurements offer a promising approach for age determination.