Acoustic-Based Spatio-Temporal Learning for Press-Fit Evaluation of Femoral Stem Implants

Seibold M, Hoch A, Suter D, Farshad M, Zingg PO, Navab N, Fuernstahl P (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12904 LNCS

Pages Range: 447-456

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Virtual, Online

ISBN: 9783030872014

DOI: 10.1007/978-3-030-87202-1_43

Abstract

In this work, we propose a method utilizing tool-integrated vibroacoustic measurements and a spatio-temporal learning-based framework for the detection of the insertion endpoint during femoral stem implantation in cementless Total Hip Arthroplasty (THA). In current practice, the optimal insertion endpoint is intraoperatively identified based on surgical experience and dependent on a subjective decision. Leveraging spectogram features and time-variant sequences of acoustic hammer blow events, our proposed solution can give real-time feedback to the surgeon during the insertion procedure and prevent adverse events in clinical practice. To validate our method on real data, we built a realistic experimental human cadaveric setup and acquired acoustic signals of hammer blows during broaching the femoral stem cavity with a novel inserter tool which was enhanced by contact microphones. The optimal insertion endpoint was determined by a standardized preoperative plan following clinical guidelines and executed by a board-certified surgeon. We train and evaluate a Long-Term Recurrent Convolutional Neural Network (LRCN) on sequences of spectrograms to detect a reached target press fit corresponding to a seated implant. The proposed method achieves an overall per-class recall of 93.82 ± 5.11 % for detecting an ongoing insertion and 70.88 ± 11.83 % for identifying a reached target press fit for five independent test specimens. The obtained results open the path for the development of automated systems for intra-operative decision support, error prevention and robotic applications in hip surgery.

Involved external institutions

University of Zurich / Universität Zürich (UZH) CH Switzerland (CH) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Seibold, M., Hoch, A., Suter, D., Farshad, M., Zingg, P.O., Navab, N., & Fuernstahl, P. (2021). Acoustic-Based Spatio-Temporal Learning for Press-Fit Evaluation of Femoral Stem Implants. In Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 447-456). Virtual, Online: Springer Science and Business Media Deutschland GmbH.

MLA:

Seibold, Matthias, et al. "Acoustic-Based Spatio-Temporal Learning for Press-Fit Evaluation of Femoral Stem Implants." Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 447-456.

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