Armando Hoch
Papers
1
Total Citations
4
H-Index
1
About
Armando Hoch is a biomedical engineer whose research focuses on the intersection of acoustic sensing, machine learning, and orthopedic implant biomechanics. His work pioneers non-invasive, real-time evaluation methods for surgical implant stability, with a particular emphasis on femoral stem fixation in total hip arthroplasty. Hoch’s most-cited paper, "Acoustic-Based Spatio-Temporal Learning for Press-Fit Evaluation of Femoral Stem Implants" (2021), introduces a novel framework that combines acoustic signal analysis with spatio-temporal deep learning to assess press-fit quality during surgery. This contribution addresses a critical gap in orthopedic practice, offering a data-driven alternative to subjective tactile feedback and potentially reducing revision rates. Though his citation count is still growing—his lead work has garnered 4 citations to date—the originality and clinical relevance of his approach have positioned him as an emerging voice in smart surgical instrumentation. Hoch’s research exemplifies how cross-disciplinary methods can transform traditional surgical workflows, and his ongoing work promises to advance the precision and safety of implant placement.
Research Focus
Key Achievements
Top Papers
- 1