Evidence map›Paper›PMID 34617023›Full record

ArticleRadiology. Artificial intelligence2021

Automated Morphometric Analysis of the Hip Joint on MRI from the German National Cohort Study.

Marc Fischer, Sven S Walter, Tobias Hepp, Manuela Zimmer, Mike Notohamiprodjo, Fritz Schick, Bin Yang

Abstract read

In one paragraph

Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it

0cells of the map it votes in

5citing papers in PubMed

–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

5 citing papers in PubMed.

Artificial Intelligence-Based Proximal Bone Shape Asymmetry Analysis and Clinical Correlation with Cartilage Relaxation Times and Functional Activity.Bioengineering (Basel, Switzerland) · 2026
Article
Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments.Scientific reports · 2025
Article
Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH.Biomedical engineering online · 2024
Article
Deep learning model for measuring the sagittal Cobb angle on cervical spine computed tomography.BMC medical imaging · 2023
Article
Automated measurement of alpha angle on 3D-magnetic resonance imaging in femoroacetabular impingement hips: a pilot study.Journal of orthopaedic surgery and research · 2022
Article

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

7 authors.

Marc FischerInstitute of Signal Processing and Systems Theory, University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany (M.F., M.Z., B.Y.); Department of Diagnostic and Interventional Radiology, Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany (S.S.W., T.H., M.N., F.S.); and Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany (T.H.).ORCID https://orcid.org/0000-0001-8892-0079

Sven S WalterInstitute of Signal Processing and Systems Theory, University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany (M.F., M.Z., B.Y.); Department of Diagnostic and Interventional Radiology, Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany (S.S.W., T.H., M.N., F.S.); and Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany (T.H.).ORCID 0000-0002-2870-825X

Tobias HeppInstitute of Signal Processing and Systems Theory, University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany (M.F., M.Z., B.Y.); Department of Diagnostic and Interventional Radiology, Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany (S.S.W., T.H., M.N., F.S.); and Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany (T.H.).ORCID https://orcid.org/0000-0002-5241-9642

Manuela ZimmerInstitute of Signal Processing and Systems Theory, University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany (M.F., M.Z., B.Y.); Department of Diagnostic and Interventional Radiology, Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany (S.S.W., T.H., M.N., F.S.); and Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany (T.H.).

Mike NotohamiprodjoInstitute of Signal Processing and Systems Theory, University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany (M.F., M.Z., B.Y.); Department of Diagnostic and Interventional Radiology, Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany (S.S.W., T.H., M.N., F.S.); and Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany (T.H.).

Fritz SchickInstitute of Signal Processing and Systems Theory, University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany (M.F., M.Z., B.Y.); Department of Diagnostic and Interventional Radiology, Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany (S.S.W., T.H., M.N., F.S.); and Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany (T.H.).ORCID 0000-0002-4231-3406

Bin YangInstitute of Signal Processing and Systems Theory, University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany (M.F., M.Z., B.Y.); Department of Diagnostic and Interventional Radiology, Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany (S.S.W., T.H., M.N., F.S.); and Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany (T.H.).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop and validate an automated morphometric analysis framework for the quantitative analysis of geometric hip joint parameters in MR images from the German National Cohort (GNC) study. MATERIALS AND

methodsA secondary analysis on 40 participants (mean age, 51 years; age range, 30-67 years; 25 women) from the prospective GNC MRI study (2015-2016) was performed. Based on a proton density-weighted three-dimensional fast spin-echo sequence, a morphometric analysis approach was developed, including deep learning-based landmark localization, bone segmentation of the femora and pelvis, and a shape model for annotation transfer. The centrum-collum-diaphyseal, center-edge (CE), three alpha angles, head-neck offset (HNO), and HNO ratio along with the acetabular depth, inclination, and anteversion were derived. Quantitative validation was provided by comparison with average manual assessments of radiologists in a cross-validation format. Paired-sample

resultsHigh agreement in mean Dice similarity coefficients was achieved (average of 97.52% ± 0.46 [standard deviation]). The subsequent morphometric analysis produced results with low mean MAD values, with the highest values of 3.34° (alpha 03:00 o'clock position) and 0.87 mm (HNO) and ICC values ranging between 0.288 (HNO ratio) and 0.858 (CE) compared with manual assessments. These values were in line with interreader agreements, which at most had MAD values of 4.02° (alpha 12:00 o'clock position) and 1.07 mm (HNO) and ICC values ranging between 0.218 (HNO ratio) and 0.777 (CE).

conclusionAutomatic extraction of geometric hip parameters from MRI is feasible using a morphometric analysis approach with deep learning.

Indexed as

AnatomyApplication DomainComputer-Aided Diagnosis (CAD)Computer Applications-3DHipInterventional-MSKMR-ImagingNeural NetworksQuantificationSegmentationSkeletal-AppendicularVision

Identifiers

PMID34617023

DOI10.1148/ryai.2021200213

PMCPMC8489451

What OpenQuestion holds

Textmetadata

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.