Papers
13
Total Citations
233
H-Index
9
About
Peter Savov is an orthopedic surgery researcher whose work sits at the intersection of robotic-assisted arthroplasty, surgical precision, and patient outcomes. His research has made substantial contributions to the field of total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA), with a particular focus on how robotic systems can improve alignment accuracy, reduce complications, and democratize surgical quality across experience levels. His most-cited work — a 2021 learning curve analysis of imageless robotic-assisted TKA with 59 citations — helped establish benchmarks for surgeons adopting robotic platforms, addressing a critical barrier to widespread adoption. His investigations into individualized and kinematic alignment strategies (39 citations) reflect a nuanced understanding that anatomical variation demands personalized implant positioning. Notably, Savov has demonstrated that robotic assistance can reduce early revision rates even for low-volume UKA surgeons, a finding with meaningful implications for healthcare equity and training. His scope extends to practical occupational health concerns, including noise exposure in robotic surgery suites, and to 3D templating in hip arthroplasty. With over 220 cumulative citations, Savov's portfolio represents a rigorous, clinically grounded effort to refine modern joint replacement surgery.
Research Focus
Key Achievements
Top Papers
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- 4Noise exposure during robot-assisted total knee arthroplasty23 citations · 2022
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- 6Imageless robotic-assisted revision arthroplasty from UKA to TKA15 citations · 2021
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