The clinical relevance of sound changes produced during cementless hip arthroplasty: a correctly sized femoral broach creates a distinctive pattern of audio frequencies directly related to bone geometry

Patients With Shorter Stature Exhibit Minimal Hammering Sound Changes During Cementless Stem Insertion in Total Hip Arthroplasty

Background

Listening to the change in the hammering sound is 1 of the elements used to assess the cementless stem stability. This study aimed to quantitatively investigate the change in the acoustic characteristics between the early and late phases of cementless stem insertion in total hip arthroplasty and to identify which patient characteristics contribute to the change in the hammering sound.

Methods

The acoustic parameters of the hammering sounds in the early and late phases of cementless taper-wedged stem insertion for 51 hips in 45 patients who underwent total hip arthroplasty (mean age = 68 years, height = 1.56 m, weight = 55.0 kg) were analyzed. Parameters including patient's basic characteristics, radiographical femoral morphology, and canal fill ratio were assessed as potential contributors to the change in the hammering sound.

Results

The low-frequency bands (0.5-1.0 kHz and 1.0-1.5 kHz) showed the largest changes during stem insertion and were therefore considered key bands for the analysis of sound alterations. Multivariate linear regression analysis showed that height (β = 8.312, P = .013) and proximal canal fill ratio (β = -3.8568, P = .038) were independently associated with the sound alterations. The decision tree analysis identified height (≥1.66 m or <1.66 m) as the best single discriminator for the sound alteration.

Conclusions

Patients with smaller stature showed the least change in the hammering sound during stem insertion. Understanding the acoustic characteristics of hammering sound alteration during cementless stem insertion may aid in the achievement of optimal stem insertion.

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

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.

Artificial intelligence for distinguishment of hammering sound in total hip arthroplasty

Recent studies have focused on hammering sound analysis during insertion of the cementless stem to decrease complications in total hip arthroplasty. However, the nature of the hammering sound is complex to analyse and varies widely owing to numerous possible variables. Therefore, we performed a preliminary feasibility study that aimed to clarify the accuracy of a prediction model using a machine learning algorithm to identify the final rasping hammering sound recorded during surgery. The hammering sound data of 29 primary THA without complication were assessed. The following definitions were adopted. Undersized rasping: all undersized stem rasping before the rasping of the final stem size, Final size rasping: rasping of the final stem size, Positive example: hammering sound during final size rasping, Negative example A: hammering sound during minimum size stem rasping, Negative example B: hammering sound during all undersized rasping. Three datasets for binary classification were set. Finally, binary classification was analysed in six models for the three datasets. The median values of the ROC-AUC in models A–F among each dataset were dataset a: 0.79, 0.76, 0.83, 0.90, 0.91, and 0.90, dataset B: 0.61, 0.53, 0.67, 0.69, 0.71, and 0.72, dataset C: 0.60, 0.48, 0.57, 0.63, 0.67, and 0.63, respectively. Our study demonstrated that artificial intelligence using machine learning was able to distinguish the final rasping hammering sound from the previous hammering sound with a relatively high degree of accuracy. Future studies are warranted to establish a prediction model using hammering sound analysis with machine learning to prevent complications in THA.

Acoustic characteristics of broaching procedure for post-operative stem subsidence in cementless total hip arthroplasty

PURPOSE

Avoiding stem subsidence is crucial for achieving better outcome for cementless total hip arthroplasty (THA). The aim of this study was to develop a prediction model for the incidence of post-operative stem subsidence using full quantitative acoustic parameters in hammering sound during the broaching procedure and to assess the accuracy of this prediction model.

METHODS

The acoustic parameters of the hammering sounds during a broaching procedure for 55 hips in 49 patients who underwent THAs with cementless taper-wedged stem were analysed. The stem subsidence was assessed at one month post-operatively, and the relationship between the acoustic parameters and the value of stem subsidence was investigated.

RESULTS

The average stem subsidence was 2.15 ± 2.91 mm. The subsidence 3 mm or more was observed in eleven hips (20%), and 5 mm or more was observed in seven hips (12.7%). Basic patient's characteristics, preoperative femoral morphology and immediate post-operative canal fill ratio and stem alignment were not significantly related to the volume of stem subsidence. Nine acoustic parameters were significantly correlated with the value of subsidence. The prediction model for post-operative subsidence using only acoustic parameters during broaching procedure was established, and this model showed a positive prediction value of 100% and a negative prediction value of 90.6% for post-operative stem subsidence at 5 mm or more.

CONCLUSION

Post-operative stem subsidence can be predicted by using acoustic parameters of the hammering sound during the broaching procedure. Our results suggest that we are at the start of a new era in which novel and innovative smart technologies can be used to assist in orthopaedic surgery.