Bone Age Estimation of Pediatrics by Analyzing Hand X-Rays Using Deep Learning Technique

The determination of bone age is critical for detecting metabolic and endocrine problems in a child's development. It provides important insights on the rate of structural and biological development, which frequently differs compared to the chronological age determined at birth. This study pres...

Full description

Saved in:
Bibliographic Details
Published in2023 International Conference on Recent Advances in Information Technology for Sustainable Development (ICRAIS) pp. 245 - 249
Main Authors Palkar, Anisha, Shanbhog, Sharisha, Medikonda, Jeevan
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.11.2023
Subjects
Bone Age Prediction
Bones
Computational modeling
Deep learning
Mean Absolute Error (MAE)
Pediatric
Pediatrics
Predictive models
Radiographs
Training
Transfer learning
Online AccessGet full text

Cover

More Information
Summary:The determination of bone age is critical for detecting metabolic and endocrine problems in a child's development. It provides important insights on the rate of structural and biological development, which frequently differs compared to the chronological age determined at birth. This study presents a completely automated deep learning technique for correctly determining bone age from X-ray images of the hand. The dataset used for training and evaluation is derived from the Radiological Society of North America's 2017 Pediatric Bone Age Challenge, which comprises left hand X-ray pictures annotated with gender and age information. To determine bone age precisely, we use a transfer learning technique using the pre-trained Xception model. By fine-tuning the neural network on the bone age dataset, it was possible to capture complicated patterns and attributes peculiar to bone development. With a mean absolute error (MAE) of 1.52 months, the experimental results show a remarkable level of convergence between the predicted age and the actual chronological age. The therapeutic significance of our suggested technique arises from its potential to be a beneficial tool to assist medical practitioners in more correctly and effectively determining bone age. The incorporation of AI-based autonomous bone age assessment can help reduce the diagnostic process and assist in early identification of developmental anomalies, ultimately contributing to improved pediatric healthcare.
DOI:10.1109/ICRAIS59684.2023.10367114