• Poojari Manish Lokanath
• Sowmya
• Abdul Ahad
• Abahy S
• Gopal Krishna Naik

AgeScan is an intelligent skeletal maturity estimation an AI-driven approach utilizing cutting-edge neural network architectures techniques to skeletal maturity assessment using children's hand radiographs. Built on the Xception model architecture, AgeScan processes medical images and outputs precise bone age estimations, supporting clinicians in the diagnosis of growth disorders. The system is trained on a publicly available RSNA bone age dataset and fine-tuned using data augmentation and hyperparameter optimization techniques. With a mean absolute error of 5.56 months in internal testing, AgeScan demonstrates clinical potential for improving accuracy and consistency in bone age interpretation, reducing dependency on manual evaluations.

Bone age, AI, Xception, deep learning, pediatric radiology, skeleton growth, RSNA dataset, medical imaging

"AGESCAN: DEEP LEARNING FOR SKELETON GROWTH ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.529-531, July-2025, Available :http://www.jetir.org/papers/JETIRGX06101.pdf

"AGESCAN: DEEP LEARNING FOR SKELETON GROWTH ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp529-531, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06101.pdf