Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring

Abstract

Bone regeneration in orthopedics is a great challenge, as efficient recovery affects patient's quality of life. Common methods encounter different limitations regarding diagnosis, implantation, and bone health monitoring. Recent advances in artificial intelligence (AI), specifically machine learning (ML) algorithms, have presented opportunities to enhance these aspects by accurately analyzing imaging data. This provides detailed evaluations that help to determine bone condition and guide treatment strategies. ML models can facilitate a patient-specific approach to select and design intelligent implants by examining extensive biomaterial datasets to identify the best match for each patient's biomechanical and biological needs. Furthermore, ML models can analyze data to accurately predict bone-healing timelines, allowing clinicians to track recovery trends, and make timely interventions in potential complications. In clinical settings, ML tools assist with preoperative planning, postoperative follow-up, and the design of intelligent implants, offering more effective, data-driven decision-making and favorable outcomes in bone regeneration. Looking ahead, while ML advancements show promise for effective orthopedic therapies, challenges persist. ML could revolutionize bone regeneration in various ways and offer predictive insights that promote patient-centered orthopedic care. Altogether, considering the continued evolution of ML, its integration into clinical applications will be crucial for developing truly intelligent bone regeneration therapies.