Evidence map›Paper›PMID 40689092›Full record

ArticleJournal of experimental orthopaedics2025

Three-dimensional reconstruction of the knee joint based on automated 1.5T magnetic resonance image segmentation: A feasibility study.

Charles Pioger, Laura Marin, Yvon Gautier, Julien Cléchet, Pierre Imbert, Christian Lutz, Étienne Cavaignac, Bertrand Sonnery-Cottet

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

8 authors.

Charles PiogerDepartment of Orthopaedic Surgery Ambroise Paré Hospital Boulogne-Billancourt France.ORCID https://orcid.org/0000-0003-1180-4955
Laura MarinAREAS Grenoble France.
Yvon GautierAREAS Grenoble France.
Julien CléchetCentre Orthopédique Santy Lyon France.
Pierre ImbertClinique Les Lauriers, ELSAN Fréjus France.
Christian LutzInstitut de Chirurgie Orthopédique et Sportive à Strasbourg Strasbourg France.
Étienne CavaignacLaboratory AMIS, UMR 5288 CNRS Paul Sabatier University Toulouse France.
Bertrand Sonnery-CottetCentre Orthopédique Santy Lyon France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To validate the accuracy of three-dimensional (3D) bone and cartilage reconstructions of the distal femur and proximal tibia derived from 1.5 Tesla magnetic resonance imaging (MRI), using fully automated and semi-automated segmentation methods, compared to surface laser scanning (LS) as the reference standard. Methods: Eleven fresh-frozen cadaveric knees were imaged using a 1.5 T MRI scanner. Manual (MS), fully automated (A), and semi-automated (SA) segmentations were performed to generate 3D models of the distal femur and proximal tibia. A transformer-based deep learning model (UNet-R) was used for automated segmentation. Laser surface scanning provided high-resolution ground-truth 3D models. Point-to-surface distances between MRI-based and LS-derived models were calculated to assess reconstruction accuracy. Bland-Altman analyses were performed to compare segmentation methods. Time to generate 3D models was recorded for each method. Results: The mean absolute point-to-surface distance for femoral models was 1.19 mm (±0.42) for MRI A, 1.05 mm (±0.09) for MRI SA, and 0.99 mm (±0.08) for MRI MS. For tibial models, the corresponding values were 1.54 mm (±1.02), 1.03 mm (±0.17), and 0.93 mm (±0.14), respectively. MRI A showed larger variability, which required manual correction. Time analysis revealed significant efficiency gains: 27 s for MRI A, 1520 s for MRI SA, and 14,191 s for MRI MS ( Conclusions: MRI-based 3D reconstructions of the knee using a 1.5 T system and semi-automated segmentation achieved sub-millimetre accuracy comparable to manual segmentation and significantly outperformed fully automated models in precision, while substantially reducing segmentation time. These findings support the integration of AI-assisted 3D reconstruction into preoperative planning workflows for knee ligament surgery, offering a reliable, radiation-free alternative to CT-based modelling. Level of Evidence: Level IV, controlled laboratory study.

Indexed as

3D knee reconstructiondeep learningmagnetic resonance imagingpreoperative planningsegmentationsurface laser scanning

Identifiers

PMID40689092
PMCPMC12272497

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.