Papers

8

Total Citations

144

H-Index

5

About

R. Michael Meneghini is a leading orthopedic surgeon and researcher whose work is reshaping the landscape of joint arthroplasty. His research focuses on the intersection of advanced technology and surgical precision, specifically in total knee arthroplasty (TKA) and total hip arthroplasty (THA). Meneghini’s major contributions lie in leveraging machine learning and artificial intelligence to establish evidence-based targets for implant positioning. His highly cited 2021 paper (52 citations) pioneered the use of machine learning algorithms to identify optimal sagittal component position in TKA, moving beyond traditional "rectangular gap" dogma. He has further demonstrated that targeting native coronal and sagittal alignment significantly improves clinical outcomes (34 citations), and his work on AI-driven 2D-to-3D bone modeling (17 citations) promises to enhance preoperative planning. Meneghini’s impact is also felt in surgical education, as he critically evaluates the effect of robotic assistance on resident training (17 citations). By championing data-driven, patient-specific alignment and biomechanics—from the knee to the hip—Meneghini is not just refining surgical technique; he is building the foundational evidence for the next generation of personalized, technology-enabled arthroplasty.

Research Focus

Key Achievements

5
H-Index
8
Papers
144
Total Citations
18
Avg Citations/Paper
🏆 Most Cited Paper
Machine Learning Algorithms Identify Optimal Sagittal Component Position in Total Knee Arthroplasty
52 citations · 2021
📈 Most Prolific Year: 2023 (4 Papers)
🤝 Key Collaborators: 21
🏛 Institutions: Indiana University Health, Indiana Orthopaedic Hospital, Joint Replacement Institute, Urology of Indiana

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago