Volume 2 - Issue 3, May - June 2026

Bone development prediction is an essential task in pediatric healthcare, orthopedics, and forensic science. Accurate prediction of skeletal maturity helps in diagnosing growth disorders, planning treatments, and estimating biological age. Traditional methods rely on manual assessment of radiographic images, which is time-consuming and subject to inter-observer variability. This research article presents a comprehensive study on the application of Long Short-Term Memory (LSTM) networks for bone development prediction. The proposed methodology utilizes sequential bone growth data to model temporal dependencies and predict future skeletal development stages. A detailed literature survey of recent studies is provided, followed by the proposed methodology, experimental results, and future research directions. The study demonstrates that LSTM-based models achieve superior prediction accuracy compared with conventional machine learning approaches due to their ability to capture long-term temporal patterns in bone growth data.

Samreen Sulthana, Nimmaraju Rajesh, Malipatel Anusha,"A Deep Learning Framework for Bone Development Prediction Using Long Short-Term Memory (LSTM) Networks" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(1312-1316) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.