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Bone & Joint Open
Vol. 5, Issue 8 | Pages 715 - 720
23 Aug 2024
Shen TS Cheng R Chiu Y McLawhorn AS Figgie MP Westrich GH

Aims

Implant waste during total hip arthroplasty (THA) represents a significant cost to the USA healthcare system. While studies have explored methods to improve THA cost-effectiveness, the literature comparing the proportions of implant waste by intraoperative technology used during THA is limited. The aims of this study were to: 1) examine whether the use of enabling technologies during THA results in a smaller proportion of wasted implants compared to navigation-guided and conventional manual THA; 2) determine the proportion of wasted implants by implant type; and 3) examine the effects of surgeon experience on rates of implant waste by technology used.

Methods

We identified 104,420 implants either implanted or wasted during 18,329 primary THAs performed on 16,724 patients between January 2018 and June 2022 at our institution. THAs were separated by technology used: robotic-assisted (n = 4,171), imageless navigation (n = 6,887), and manual (n = 7,721). The primary outcome of interest was the rate of implant waste during primary THA.


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.


Bone & Joint Open
Vol. 2, Issue 10 | Pages 834 - 841
11 Oct 2021
O'Connor PB Thompson MT Esposito CI Poli N McGree J Donnelly T Donnelly W

Aims

Pelvic tilt (PT) can significantly change the functional orientation of the acetabular component and may differ markedly between patients undergoing total hip arthroplasty (THA). Patients with stiff spines who have little change in PT are considered at high risk for instability following THA. Femoral component position also contributes to the limits of impingement-free range of motion (ROM), but has been less studied. Little is known about the impact of combined anteversion on risk of impingement with changing pelvic position.

Methods

We used a virtual hip ROM (vROM) tool to investigate whether there is an ideal functional combined anteversion for reduced risk of hip impingement. We collected PT information from functional lateral radiographs (standing and sitting) and a supine CT scan, which was then input into the vROM tool. We developed a novel vROM scoring system, considering both seated flexion and standing extension manoeuvres, to quantify whether hips had limited ROM and then correlated the vROM score to component position.