Over 8000 total hip arthroplasties (THA) in the UK were revised in 2019, half for aseptic loosening. It is believed that Artificial Intelligence (AI) could identify or predict failing THA and result in early recognition of poorly performing implants and reduce patient suffering.

The aim of this study is to investigate whether Artificial Intelligence based machine learning (ML) / Deep Learning (DL) techniques can train an algorithm to identify and/or predict failing uncemented THA.

Consent was sought from patients followed up in a single design, uncemented THA implant surveillance study (2010–2021). Oxford hip scores and radiographs were collected at yearly intervals. Radiographs were analysed by 3 observers for presence of markers of implant loosening/failure: periprosthetic lucency, cortical hypertrophy, and pedestal formation.

DL using the RGB ResNet 18 model, with images entered chronologically, was trained according to revision status and radiographic features. Data augmentation and cross validation were used to increase the available training data, reduce bias, and improve verification of results.

184 patients consented to inclusion. 6 (3.2%) patients were revised for aseptic loosening. 2097 radiographs were analysed: 21 (11.4%) patients had three radiographic features of failure.

166 patients were used for ML algorithm testing of 3 scenarios to detect those who were revised. 1) The use of revision as an end point was associated with increased variability in accuracy. The area under the curve (AUC) was 23–97%. 2) Using 2/3 radiographic features associated with failure was associated with improved results, AUC: 75–100%. 3) Using 3/3 radiographic features, had less variability, reduced AUC of 73%, but 5/6 patients who had been revised were identified (total 66 identified).

The best algorithm identified the greatest number of revised hips (5/6), predicting failure 2–8 years before revision, before all radiographic features were visible and before a significant fall in the Oxford Hip score. True-Positive: 0.77, False Positive: 0.29.

ML algorithms can identify failing THA before visible features on radiographs or before PROM scores deteriorate. This is an important finding that could identify failing THA early.

Introduction

The Opera acetabular component (Smith & Nephew Ltd) shares many same design features with the Ogee (Depuy Int.). Differences are a malleable flange, instrumentation designed to improve cement pressurisation and the specifics of the UHMW-poly used.

Litigation costs are significant and increasing annually within the National Health Service (NHS) in England.

The aim of this work was to evaluate the burden of successful litigation relating to hip surgery in England. Secondary measures looked at identifying the commonest causes of successful legal action.

A retrospective review was conducted on the National Health Service Litigation Authority (NHSLA) database. All successful claims related to hip surgery over a 10 year period from 2003–2013 were identified. A total of 798 claims were retrieved and analysed.

The total cost of successful claims to the NHS was £66.3 million. This compromised £59 million in damages and £7.3 million in NHS defence-related legal costs. The mean damages for settling a claim were £74,026 (range £197-£1.6million). The commonest cause of claim was post-operative pain with average damages paid in relation to this injury being £99,543. Nerve damage and intra-operative fractures were the next commonest cause of claim with average damages settled at £103,465.

Legal action in relation to hip surgery is a considerable source of cost to the NHS. The complexity of resolving these cases is reflected in the associated legal costs which represent a significant proportion of payouts. With improved understanding of factors instigating successful legal proceedings, physicians can recognise areas where practice and training need to be improved and steps can be taken to minimise complications leading to claims.