ArticleArthroplasty today2024
Does Artificial Intelligence Outperform Humans Using Fluoroscopic-Assisted Computer Navigation for Total Hip Arthroplasty?
Article in Arthroplasty today, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Optimizing arthroplasty outcomes: the impact of artificial intelligence and robotic assistance.Annals of medicine and surgery (2012) · 2026Review
- Artificial Intelligence Applications in Implant Positioning, Dislocation Risk Prediction, and Surgical Indications in Orthopaedic Surgery.Bioengineering (Basel, Switzerland) · 2026Review
- Current Uses for Medical Imaging With Orthopaedic Implant Technology.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Review
- Geriatric Total Hip Arthroplasty: Fixation Strategies, Approaches and Outcomes.Journal of orthopaedics and sports medicine · 2026Article
- Technology in Total Hip Arthroplasty: Recent Advancements.Orthopedic reviews · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Successful total hip arthroplasty (THA) relies on the correct implant position. THA accuracy can be improved with the use of intraoperative fluoroscopic-assisted computer navigation. Artificial intelligence (AI) software may enhance fluoroscopic navigation; however, the accuracy of the AI compared to human-controlled software in assessing acetabular component position and leg length discrepancy (LLD) has not been studied. Methods: We analyzed 420 consecutive primary THAs performed by a single surgeon using fluoroscopic-assisted computer navigation software. The first cohort of 211 patients required inputs from a human technician (manual), while the second cohort of 209 patients used an automated version of the software controlled by AI. The intraoperative acetabular component placement (inclination and anteversion) and LLD were recorded and compared to the 2-week postoperative standing anterior-posterior pelvis radiograph. Results: Ninety-four percent (199/211) of cups in the manual cohort and 95% (198/209) of cups in the AI cohort were within the Lewinnek "safe-zone" ( Conclusions: Both AI and human-controlled versions of the same navigation platform were similarly accurate for navigating cup position within the Lewinnek "safe zone" and LLD accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.