ArticleJournal of experimental orthopaedics2025
Artificial intelligence-guided assessment of the hip-knee-ankle angle shows excellent correlation with experienced human raters.
Article in Journal of experimental orthopaedics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Convolutional Neural Networks in Radiology: Principles, Clinical Applications, and a Practical Framework for Radiologists.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2026Review
- Lumbar and Thoracolumbar Curves Are Associated with Coronal Lower Limb Malalignment in Adolescent Idiopathic Scoliosis.Medicina (Kaunas, Lithuania) · 2026Article
- Malrotated lateral radiographs do not allow for proper assessment of patellar height using the Caton-Deschamps Index.Journal of experimental orthopaedics · 2026Article
- Artificial intelligence-guided assessment of the hip-knee-ankle angle shows excellent correlation with experienced human raters.Journal of experimental orthopaedics · 2025Article
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
Purpose: The hip-knee-ankle angle is a crucial parameter in orthopaedic surgery for lower limb assessment. However, traditional methods for measuring the hip-knee-ankle angle on standing long-leg anteroposterior radiographs are time-consuming, require significant expertise and lack reproducibility. Given the emergence of artificial intelligence as a promising tool to automate these measurements, this study aimed to assess the accuracy of Gleamer BoneMetrics for hip-knee-ankle angle measurement and its correlation with assessments by experienced orthopaedic surgeons. Methods: A total of 75 patients who underwent knee arthroplasty between October 2023 and June 2024 were included. Preoperative and postoperative long-leg anteroposterior radiographs were analysed both by two experienced orthopaedic surgeons who manually measured the hip-knee-ankle angle and by the Gleamer BoneMetrics software; the analyses were tested for both inter- and intra-rater reliability. Statistical analysis was performed using intraclass correlation coefficients and Bland-Altman plots to assess the correlation between the Gleamer BoneMetrics and the human raters. Results: The Gleamer BoneMetrics demonstrated excellent inter- and intra-rater reliability, with intraclass correlation coefficient values ranging from 0.995 to 0.998, which were comparable to the surgeons' measurements of 0.997-0.998. The Gleamer BoneMetrics's intra-rater reliability was also excellent, with intraclass correlation coefficient values of 1.0 preoperatively and 0.996 postoperatively. Bland-Altman analysis showed minimal measurement discrepancies between Gleamer BoneMetrics and the human raters. However, in 2% of the cases ( Conclusion: The artificial intelligence-based BoneMetrics software offers an efficient and accurate method for hip-knee-ankle angle measurement, with performance comparable to experienced orthopaedic surgeons. While promising, further development is necessary to address cases in which image quality or positioning issues prevent automated measurement and to reduce reliance on human quality control. Level of Evidence: Level III.
Indexed as
Identifiers
What OpenQuestion holds
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.