Evidence map›Paper›PMID 42675753›Full record

ArticleMedicine2026

Diagnostic accuracy of intelligent quick MR reconstruction for grading posterior cruciate ligament injuries: A retrospective study with arthroscopic correlation.

Xinting Peng, Yifan Xu, Yuanling Yang, Jingjing Wei, Shenglian Wen, Lixuan Huang, Li Li, Zisan Zeng

Abstract read
In one paragraph

Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xinting PengDepartment of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.ORCID 0009-0005-5231-3126
Yifan XuDepartment of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yuanling YangDepartment of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Jingjing WeiDepartment of Radiology, The People's Hospital of Hechi, Hechi, China.
Shenglian WenDepartment of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Lixuan HuangDepartment of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Li LiDepartment of Radiology, The First Affiliated Hospital of Guilin Medical University, Guilin, China.
Zisan ZengDepartment of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.ORCID 0000-0002-6977-1175

Funding

Joint Project on Guangxi Key Research and Development Plan No. GK AB24010172Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation under Grant No.2024GXNSFAA010315
6 · The paper itself

Abstract

This retrospective diagnostic accuracy study aimed to evaluate the image quality and diagnostic accuracy of intelligent quick magnetic resonance(IQMR) reconstruction for grading posterior cruciate ligament (PCL) injuries, using arthroscopy as the reference standard. A total of 110 patients with arthroscopically confirmed PCL injuries were included, and their preoperative magnetic resonance images were processed with both standard and IQMR reconstructions. Two independent radiologists assessed subjective image quality using a 4-point Likert scale and objective metrics including signal-to-noise ratio and contrast-to-noise ratio. Diagnostic performance for detecting complete (grade 3) and partial (grade 1 and 2) tears was analyzed using receiver operating characteristic curves, and interobserver agreement was evaluated with weighted kappa statistics. Compared with standard reconstruction, IQMR reconstruction yielded significantly superior subjective and objective image quality (all P < 0.005) and excellent interobserver agreement (weighted kappa = 0.63-0.97). Diagnostic correlation with arthroscopy showed robust performance for complete tears (area under the curve = 0.79, P < 0.05) but limited efficacy for partial tears (area under the curve = 0.54 and 0.17 for grade 1 and 2, respectively), with overall moderate agreement between magnetic resonance imaging and arthroscopic grading (kappa = 0.57). In conclusion, IQMR reconstruction significantly improves image quality for PCL assessment, leading to excellent diagnostic reliability for complete tears, while its performance for partial tears remains limited. Further prospective multi-center studies are warranted.

Indexed as

ArthroscopyKnee InjuriesMagnetic Resonance ImagingPosterior Cruciate LigamentAdolescentAdultFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedObserver VariationReproducibility of ResultsRetrospective StudiesROC CurveYoung AdultDiagnostic efficacyIntelligent quick magnetic resonanceKnee injuryPosterior cruciate ligament

Identifiers

PMID42675753
PMCPMC13529156

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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.