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Bone & Joint Open
Vol. 5, Issue 8 | Pages 671 - 680
14 Aug 2024
Fontalis A Zhao B Putzeys P Mancino F Zhang S Vanspauwen T Glod F Plastow R Mazomenos E Haddad FS

Aims. Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680


Bone & Joint Open
Vol. 4, Issue 6 | Pages 416 - 423
2 Jun 2023
Tung WS Donnelley C Eslam Pour A Tommasini S Wiznia D

Aims

Computer-assisted 3D preoperative planning software has the potential to improve postoperative stability in total hip arthroplasty (THA). Commonly, preoperative protocols simulate two functional positions (standing and relaxed sitting) but do not consider other common positions that may increase postoperative impingement and possible dislocation. This study investigates the feasibility of simulating commonly encountered positions, and positions with an increased risk of impingement, to lower postoperative impingement risk in a CT-based 3D model.

Methods

A robotic arm-assisted arthroplasty planning platform was used to investigate 11 patient positions. Data from 43 primary THAs were used for simulation. Sacral slope was retrieved from patient preoperative imaging, while angles of hip flexion/extension, hip external/internal rotation, and hip abduction/adduction for tested positions were derived from literature or estimated with a biomechanical model. The hip was placed in the described positions, and if impingement was detected by the software, inspection of the impingement type was performed.


Bone & Joint Open
Vol. 4, Issue 1 | Pages 3 - 12
4 Jan 2023
Hardwick-Morris M Twiggs J Miles B Al-Dirini RMA Taylor M Balakumar J Walter WL

Aims

Iliopsoas impingement occurs in 4% to 30% of patients after undergoing total hip arthroplasty (THA). Despite a relatively high incidence, there are few attempts at modelling impingement between the iliopsoas and acetabular component, and no attempts at modelling this in a representative cohort of subjects. The purpose of this study was to develop a novel computational model for quantifying the impingement between the iliopsoas and acetabular component and validate its utility in a case-controlled investigation.

Methods

This was a retrospective cohort study of patients who underwent THA surgery that included 23 symptomatic patients diagnosed with iliopsoas tendonitis, and 23 patients not diagnosed with iliopsoas tendonitis. All patients received postoperative CT imaging, postoperative standing radiography, and had minimum six months’ follow-up. 3D models of each patient’s prosthetic and bony anatomy were generated, landmarked, and simulated in a novel iliopsoas impingement detection model in supine and standing pelvic positions. Logistic regression models were implemented to determine if the probability of pain could be significantly predicted. Receiver operating characteristic curves were generated to determine the model’s sensitivity, specificity, and area under the curve (AUC).


Bone & Joint Open
Vol. 2, Issue 11 | Pages 988 - 996
26 Nov 2021
Mohtajeb M Cibere J Mony M Zhang H Sullivan E Hunt MA Wilson DR

Aims

Cam and pincer morphologies are potential precursors to hip osteoarthritis and important contributors to non-arthritic hip pain. However, only some hips with these pathomorphologies develop symptoms and joint degeneration, and it is not clear why. Anterior impingement between the femoral head-neck contour and acetabular rim in positions of hip flexion combined with rotation is a proposed pathomechanism in these hips, but this has not been studied in active postures. Our aim was to assess the anterior impingement pathomechanism in both active and passive postures with high hip flexion that are thought to provoke impingement.

Methods

We recruited nine participants with cam and/or pincer morphologies and with pain, 13 participants with cam and/or pincer morphologies and without pain, and 11 controls from a population-based cohort. We scanned hips in active squatting and passive sitting flexion, adduction, and internal rotation using open MRI and quantified anterior femoroacetabular clearance using the β angle.


Bone & Joint Open
Vol. 2, Issue 8 | Pages 631 - 637
10 Aug 2021
Realpe AX Blackstone J Griffin DR Bing AJF Karski M Milner SA Siddique M Goldberg A

Aims

A multicentre, randomized, clinician-led, pragmatic, parallel-group orthopaedic trial of two surgical procedures was set up to obtain high-quality evidence of effectiveness. However, the trial faced recruitment challenges and struggled to maintain recruitment rates over 30%, although this is not unusual for surgical trials. We conducted a qualitative study with the aim of gathering information about recruitment practices to identify barriers to patient consent and participation to an orthopaedic trial.

Methods

We collected 11 audio recordings of recruitment appointments and interviews of research team members (principal investigators and research nurses) from five hospitals involved in recruitment to an orthopaedic trial. We analyzed the qualitative data sets thematically with the aim of identifying aspects of informed consent and information provision that was either unclear, disrupted, or hindered trial recruitment.