Is it feasible to develop a supervised learning algorithm incorporating spinopelvic mobility to predict impingement in patients undergoing total hip arthroplasty? a proof-of-concept study

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 o...

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Vydáno v:Bone & joint open Ročník 5; číslo 8; s. 671 - 680
Hlavní autoři: Fontalis, Andreas, Zhao, Baixiang, Putzeys, Pierre, Mancino, Fabio, Zhang, Shuai, Vanspauwen, Thomas, Glod, Fabrice, Plastow, Ricci, Mazomenos, Evangelos, Haddad, Fares S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London The British Editorial Society of Bone & Joint Surgery 14.08.2024
Témata:
acetabular component
Arthroplasty
artificial intelligence
biomechanics
flexion
Hip
hips
impingement
offset
pelvic incidence
pelvis
prospective cohort study
radiographs
Reverse Hybrid
robotic arm
robotic-arm assistance
sacral slope
spinopelvic mobility
supervised learning
total hip arthroplasty
total hip arthroplasty (tha)
virtual range of motion
ISSN:2633-1462, 2633-1462
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Shrnutí: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.
Bibliografie:A. Fontalis reports that this work was supported by a scholarship from the Onassis Foundation (F ZR 065-1/2021-2022). A. Fontalis also reports support from Stryker towards the EFORT Robotic Fellowship, and a Freemasons' Royal Arch Fellowship with support from the Arthritis Research Trust, both unrelated to this work. F. S. Haddad reports: multiple research study grants from Stryker, Smith & Nephew, Corin, the National Institute for Health and Care Research (NIHR), and the International Olympic Committee; royalties or licenses from Stryker, Smith & Nephew, Corin, and MatOrtho; consulting fees from Stryker; speaker payments or honoraria from Stryker, Smith & Nephew, Zimmer, AO Recon, and Mathys; and support for attending meetings and/or travel from Stryker, Mathys, AO Recon, and The Bone & Joint Journal, all of which are unrelated to this work. F. S. Haddad is also Editor-in-Chief of The Bone & Joint Journal, President of the International Hip Society, and Vice President of the European Hip Society. E. Mazomenos reports an institutional payment from Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) (203145z/16/z,NS/A000050/1) for this work. R. Plastow reports an unpaid educational role with the Royal College of Surgeons RADAR initiative. P. Putzeys reports royalties from Conmed, and consulting fees, lecture payments, and travel payment for attending meetings from Stryker, all of which are unrelated to this work. S. Zhang reports an institutional payment from NIHR UCLH Biomedical Research Centre (NIHR203328) for this work. B. Zhao reports a Wellcome Trust Innovator Award (223793/21/Z) for this work.
ISSN:2633-1462
2633-1462
DOI:10.1302/2633-1462.58.BJO-2024-0020.R1