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22-11-2022 | Hip-TEP | Original Paper

Highly accurate acoustical prediction using support vector machine algorithm for post-operative subsidence after cementless total hip arthroplasty

Authors: Yasuhiro Homma, Xu Zhuang, Hiroshi Ohtsu, Seiya Ishii, Yuichi Shirogane, Koju Hayashi, Taiji Watari, Tomonori Baba, Muneaki Ishijima

Published in: International Orthopaedics | Issue 1/2023

Abstract

Purpose

Acoustic analysis has recently been applied to cementless total hip arthroplasty (THA). The aim of this study was to develop a machine learning algorithm to predict post-operative subsidence with high accuracy.

Methods

The acoustic parameters of the hammering sounds during a broaching procedure for 62 hips in 55 patients who underwent THAs with cementless taper-wedged stem were analysed. The patient’s basic background such as age, sex, height, weight and body mass index, the femoral morphological parameters and the hammering sound characteristics of 24 features of normalised sound pressure (nSP) in 24 frequency ranges were applied to binary classification using a support vector machine using the following models with different features: model A, nSP only; model B, nSP + patients’ basic background features; model C, nSP + patients’ basic background features + femoral morphological parameters.

Results

In 62 hips with 310 hammering sounds, 12 hips (19.4%) showed ≥ 3 mm of post-operative subsidence; hence, 60 hammering sounds were set as positive examples and 250 hammering sounds were set as negative examples. The AUC was very high in all models. The accuracy (AUC/sensitivity/specificity/positive predictive value/negative predictive value/accuracy rate) of each model was as follows: model A, 0.963/0.656/0.996/0.980/0.925/0.934; model B, 0.9866/0.675/1.000/1.000/0.928/0.937 and model C, 0.998/0.750/1.000/1.000/0.950/0.957.

Conclusion

In this study, we developed a high-accuracy machine learning algorithm for post-operative subsidence using acoustic parameters and additional pre-operative features. Our results represent a step toward the realisation of acoustic monitoring to avoid the complication in cementless THA.

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Title: Highly accurate acoustical prediction using support vector machine algorithm for post-operative subsidence after cementless total hip arthroplasty
Authors: Yasuhiro Homma
Xu Zhuang
Hiroshi Ohtsu
Seiya Ishii
Yuichi Shirogane
Koju Hayashi
Taiji Watari
Tomonori Baba
Muneaki Ishijima
Publication date: 22-11-2022
Publisher: Springer Berlin Heidelberg
Keywords: Hip-TEP
Hip-TEP
Published in: International Orthopaedics / Issue 1/2023
Print ISSN: 0341-2695
Electronic ISSN: 1432-5195
DOI: https://doi.org/10.1007/s00264-022-05641-5

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Highly accurate acoustical prediction using support vector machine algorithm for post-operative subsidence after cementless total hip arthroplasty

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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