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 \(93.82\pm 5.11\%\) for detecting an ongoing insertion and \(70.88\pm 11.83\%\) 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.
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References
Abdel, M.P., Houdek, M.T., Watts, C.D., Lewallen, D.G., Berry, D.J.: Epidemiology of periprosthetic femoral fractures in 5417 revision total hip arthroplasties: a 40-year experience. Bone Joint J. 98, 468–474 (2016)
Berend, K.R., Lombardi, A.V., Mallory, T.H., Chonko, D.J., Dodds, K.L., Adams, J.B.: Cerclage wires or cables for the management of intraoperative fracture associated with a cementless, tapered femoral prosthesis: results at 2 to 16 years. J. Arthroplasty 19, 17–21 (2004)
Capello, W.N., Houdek, M.T., Watts, C.D., Lewallen, D.G., Berry, D.J.: Periprosthetic fractures around a cementless hydroxyapatite-coated implant: a new fracture pattern is described. Clin. Orthop. Relat. Res. 472, 604–610 (2014)
Dubory, A., Rosi, G., Tijou, A., Lomami, H.A., Flouzat-Lachaniette, C.H., Haiat, G.: A cadaveric validation of a method based on impact analysis to monitor the femoral stem insertion. J. Mech. Behav. Biomed. Mater. 103, 103535 (2020)
Goossens, Q., Leuridan, S., Roosen, J.: Monitoring of reamer seating using acoustic information. In: Annual meeting of the European Society of Biomechanics (2015)
Goossens, Q., et al.: Acoustic analysis to monitor implant seating and early detect fractures in cementless THA: an in vivo study. J. Orthop. Res. 39, 1164–1173 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Hershey, S., et al.: CNN architectures for large-scale audio classification. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135 (2017)
Illanes, A., et al.: Novel clinical device tracking and tissue event characterization using proximally placed audio signal acquisition and processing. Sci. Rep. 8, 12070 (2018)
Le Béguec, P., Canovas, F., Roche, O., Goldschild, M., Batard, J.: Uncemented Femoral Stems for Revision Surgery. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-03614-4
Lin, W., Hasenstab, K., Cunha, G.M., Schwartzman, A.: Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment. Sci. Rep. 10, 20336 (2020)
Lyon, D.A.: The discrete fourier transform, part 4: spectral leakage. J. Object Technol. 8(7), 23–34 (2009)
McFee, B., et al.: librosa: audio and music signal analysis in python. In: 14th Python in Science Conference, pp. 18–25 (2015)
Morohashi, I., et al.: Acoustic pattern evaluation during cementless hip arthroplasty surgery may be a new method for predicting complications. In: SICOT-J 3 (2017)
Oberst, S., et al.: Vibro-acoustic and nonlinear analysis of cadavric femoral bone impaction in cavity preparations. Int. J. Mech. Sci. 144, 739–745 (2018)
Ostler, D., et al.: Acoustic signal analysis of instrument-tissue interaction for minimally invasive interventions. Int. J. Comput. Assist. Radiol. Surg. 15, 771–779 (2020). https://doi.org/10.1007/s11548-020-02146-7
Purwins, H., Li, B., Virtanen, T., Schlüter, J., Chang, S.y., Sainath, T.: Deep learning for audio signal processing. IEEE J. Sel. Top. Signal Process. 14, 206–219 (2019)
Renner, L., Janz, V., Perka, C., Wassilew, G.I.: What do we get from navigation in primary THA? EFORT Open Rev. 1, 205–210 (2016)
Ricioli, W., Queiroz, M.C., Guimarães, R.P., Honda, E.K., Polesello, G., Fucs, P.M.M.B.: Prevalence and risk factors for intra-operative periprosthetic fractures in one thousand eight hundred and seventy two patients undergoing total hip arthroplasty: a cross-sectional study. Int. Orthopaedics 39(10), 1939–1943 (2015). https://doi.org/10.1007/s00264-015-2961-x
Seibold, M., et al.: Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery. Sci. Rep. 11, 3993 (2021)
Suehn, T., Pandey, A., Friebe, M., Illanes, A., Boese, A., Lohman, C.: Acoustic sensing of tissue-tool interactions - potential applications in arthroscopic surgery. Curr. Dir. Biomed. Eng. 6, 20203152 (2020)
Tijou, A.: Monitoring cementless femoral stem insertion by impact analyses: an in vitro study. J. Mech. Behav. Biomed. Mater. 88, 102–108 (2018)
Yun, H.H., Lim, J.T., Yang, S.H., Park, P.S.: Occult periprosthetic femoral fractures occur frequently during a long, trapezoidal, double-tapered cementless femoral stem fixation in primary THA. PLos ONE 19, e0221731 (2019)
Acknowledgment
This work is part of the SURGENT project and was funded by University Medicine Zurich/Hochschulmedizin Zürich. Matthias Seibold and Nassir Navab are partly funded by the Balgrist Foundation in form of the guest professorship at Balgrist University Hospital.
We would like to thank Navid Navab from Topological Media Lab at Concordia University, Montreal, Canada, for initial discussions, as well as creative and valuable interactions.
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Seibold, M. et al. (2021). Acoustic-Based Spatio-Temporal Learning for Press-Fit Evaluation of Femoral Stem Implants. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_43
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