AI-Optimized Topology for Reducing Periprosthetic Bone Loss in Total Joint Replacements

Sushma Jaiswal

Guru Ghasidas Central University, Bilaspur, India

Biography :

Sushma Jaiswal, is an Assistant Professor in the Department of Computer Science & IT at Guru Ghasidas Vishwavidyalaya, Bilas­pur. She holds a Ph.D. from RGPV and a D.Sc. in Computer Science & Engineering (2024). With over 21 years of experience, her expertise includes AI, Machine Learning, and Digital Image Processing. A prolific inventor, she holds a world record for the highest number of patents and copyrights filed by an individual, grants across the UK, Australia, Germany, and South Africa. She has published 150+ research papers, authored 40 books, and mentored multiple Ph.D. scholars. Recognized as a “Best Women Scientist,” she also serves as an International Educa­tional Ambassador and reviewer for IEEE and Springer journals. Her leadership includes roles as NSS Program Officer and Alum­ni Coordinator. She was notably offered a Research Engineer role for Google’s R&D projects in 2017.

Abstract :

Objective: To design a femoral stem that mimics the me­chanical properties of natural cortical bone using AI-driv­en topology optimization, thereby mitigating periprosthetic bone loss caused by stress shielding.

Methods: We utilized a Generative Adversarial Network (GAN) integrated with a Physics-Informed Neural Network (PINN) to evolve the internal lattice structure of a Titani­um-alloy (Ti6Al4V) hip stem. The AI was tasked with maxi­mizing the “Strain Energy Density” (SED) in the surrounding periprosthetic bone while maintaining a safety factor of 2.0 against fatigue failure. Over 10,000 “In Silico” loading cycles (simulating walking, climbing stairs, and stumbling) were processed to refine the topology.

Results: The AI-optimized “biomimetic” stem featured a functionally graded porous architecture, with higher poros­ity in the proximal region and a dense core. Compared to traditional solid stems, the optimized design increased load transfer to the proximal femur by 64%.

Conclusion: AI-optimized topology allows for a “mechanical match” between implant and bone, significantly reducing the risk of long-term aseptic loosening and the need for revision surgery.