Matthias Seibold
University Hospital of Zurich, Technical University of Munich
Papers
2
Total Citations
26
H-Index
2
About
Matthias Seibold is a leading researcher in surgical data science and medical cyber-physical systems, with a focus on acoustic signal analysis for minimally invasive interventions. His work pioneers the use of sound as a non-visual feedback modality to enhance instrument–tissue interaction awareness during surgery, addressing critical challenges in unintuitive instrument handling. Seibold’s most cited paper, "Acoustic Signal Analysis of Instrument–Tissue Interaction for Minimally Invasive Interventions" (2020, 22 citations), demonstrates how acoustic signatures can improve surgical precision and safety. He further advanced this paradigm with "Acoustic-Based Spatio-Temporal Learning for Press-Fit Evaluation of Femoral Stem Implants" (2021, 4 citations), applying machine learning to interpret acoustic patterns for orthopedic implant stability assessment. By integrating spatio-temporal learning with acoustic sensing, Seibold’s contributions enable real-time, objective feedback in surgical workflows, reducing reliance on tactile or visual cues. His work has significant implications for training, automation, and outcome optimization in minimally invasive and robotic surgery. With growing citation impact, Seibold is establishing himself as a key innovator at the intersection of acoustics, machine learning, and surgical technology.
Research Focus
Key Achievements
Top Papers
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