Papers
6
Total Citations
110
H-Index
4
About
Evan R. Deckard is an orthopedic researcher whose work is reshaping how surgeons approach joint replacement, with a focus on total knee arthroplasty (TKA) and total hip arthroplasty (THA). His key research areas include optimizing implant positioning, replicating native joint biomechanics, and leveraging machine learning to improve surgical outcomes. Deckard’s most impactful contribution comes from his 2021 study, “Machine Learning Algorithms Identify Optimal Sagittal Component Position in Total Knee Arthroplasty” (52 citations), which pioneered data-driven targets for component alignment. He further advanced the field by demonstrating that targeting native coronal and sagittal alignment in TKA optimizes clinical outcomes (34 citations), and that balancing the asymmetric native knee flexion gap promotes superior results (14 citations). His work also extends to hip arthroplasty, where he investigates whether replicating native hip biomechanics improves patient-reported outcomes, and to unicompartmental knee arthroplasty, where he has shown that experienced surgeons can match robotic accuracy in manual procedures. With a growing citation record and a focus on evidence-based, patient-specific surgical targets, Deckard is a rising voice in the movement to make joint replacement more precise, personalized, and outcome-driven.
Research Focus
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Top Papers
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