Evidence map›Paper›PMID 40361057›Full record

ArticleBMC musculoskeletal disorders2025

Rethinking femoral neck anteversion assessment: a novel automated 3D CT method compared to traditional manual techniques.

Honghu Xiao, Sutuke Yibulayimu, Chunpeng Zhao, Yudi Sang, Yimin Chen, Yufeng Ge, Qingnan Sun, Yang Ming, Mingjian Bei, Gang Zhu and 3 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC musculoskeletal disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Honghu XiaoDepartment of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China.
Sutuke YibulayimuKey Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Chunpeng ZhaoDepartment of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China. zhaochunpeng_jst@163.com.
Yudi SangBeijing Rossum Robot Technology Co., Ltd, Beijing, China.
Yimin ChenDepartment of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China.
Yufeng GeDepartment of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China.
Qingnan SunDepartment of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China.
Yang MingDepartment of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China.
Mingjian BeiDepartment of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China.
Gang ZhuBeijing Rossum Robot Technology Co., Ltd, Beijing, China.
Yingchun SongBeijing Rossum Robot Technology Co., Ltd, Beijing, China.
Yu WangKey Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Xinbao WuDepartment of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, National Center for Orthopaedics, Beijing, 100035, China.

Funding

Beijing Hospitals Authority Clinical Medicine Development of special funding support YGLX202512Beijing Jishuitan Research Funding ZR-202504National Natural Science Foundation of China 62471322Natural Science Foundation of Beijing Municipality L222136
6 · The paper itself

Abstract

purposeTo evaluate the accuracy and reliability of a novel automated 3D CT-based method for measuring femoral neck anteversion (FNA) compared to three traditional manual methods.

methodsA total of 126 femurs from 63 full-length CT scans (35 men and 28 women; average age: 52.0 ± 14.7 years) were analyzed. The automated method used a deep learning network for femur segmentation, landmark identification, and anteversion calculation, with results generated based on two axes: Auto_GT (using the greater trochanter-to-intercondylar notch center axis) and Auto_P (using the piriformis fossa-to-intercondylar notch center axis). These results were validated through manual landmark annotation. The same dataset was assessed using three conventional manual methods: Murphy, Reikeras, and Lee methods. Intra- and inter-observer reliability were assessed using intraclass correlation coefficients (ICCs), and pairwise comparisons analyzed correlations and differences between methods.

resultsThe automated methods produced consistent FNA measurements (Auto_GT: 17.59 ± 9.16° vs. Auto_P: 17.37 ± 9.17° on the right; 15.08 ± 9.88° vs. 14.84 ± 9.90° on the left). Intra-observer ICCs ranged from 0.864 to 0.961, and inter-observer ICCs between Auto_GT and the manual methods were high, except for the Lee method. No significant differences were observed between the two automated methods or between the automated and manual verification methods. Moreover, strong correlations (R > 0.9, p < 0.001) were found between Auto_GT and the manual methods.

conclusionThe novel automated 3D CT-based method demonstrates strong reproducibility and reliability for measuring femoral neck anteversion, with performance comparable to traditional manual techniques. These results indicate its potential utility for preoperative planning, postoperative evaluation, and computer-assisted orthopedic procedures. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Bone AnteversionFemur NeckImaging, Three-DimensionalTomography, X-Ray ComputedAdultAgedDeep LearningFemaleHumansMaleMiddle AgedReproducibility of Results3D CTAutomated measurementDeep learningFemoral neck anteversion

Identifiers

PMID40361057
PMCPMC12070627

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.