Evidence map›Paper›PMID 40493057›Full record

ArticleSkeletal radiology2025

Arthroscopy-validated diagnostic performance of sub-5-min deep learning super-resolution 3T knee MRI in children and adolescents.

Jan Vosshenrich, Hanns-Christian Breit, Ricardo Donners, Markus M Obmann, Dorothee Harder, Shivani Ahlawat, Sven S Walter, Aline Serfaty, Tatiane Cantarelli Rodrigues, Michael Recht and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Skeletal radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

12 authors.

Jan VosshenrichDepartment of Radiology, Grossman School of Medicine, New York University, 660 1 Ave, 3 Floor, Rm 313, New York, NY, 10016, USA.
Hanns-Christian BreitDepartment of Radiology, University Hospital Basel, Basel, Switzerland.
Ricardo DonnersDepartment of Radiology, University Hospital Basel, Basel, Switzerland.
Markus M ObmannDepartment of Radiology, University Hospital Basel, Basel, Switzerland.
Dorothee HarderDepartment of Radiology, University Hospital Basel, Basel, Switzerland.
Shivani AhlawatRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Sven S WalterDepartment of Radiology, University Hospital Tübingen, Tübingen, Germany.
Aline SerfatyMedscanlagos Radiology, Cabo Frio, Brazil.
Tatiane Cantarelli RodriguesDepartment of Radiology, Hospital Do Coraçao, São Paulo, Brazil.
Michael RechtDepartment of Radiology, Grossman School of Medicine, New York University, 660 1 Ave, 3 Floor, Rm 313, New York, NY, 10016, USA.
Steven E SternCentre for Data Analytics, Bond University, Gold Coast, Australia.
Jan FritzDepartment of Radiology, Grossman School of Medicine, New York University, 660 1 Ave, 3 Floor, Rm 313, New York, NY, 10016, USA. janfritz777@gmail.com.ORCID http://orcid.org/0000-0003-4456-3043

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aims to determine the diagnostic performance of sub-5-min combined sixfold parallel imaging (PIx3)-simultaneous multislice (SMSx2)-accelerated deep learning (DL) super-resolution 3T knee MRI in children and adolescents. MATERIALS AND

methodsChildren with painful knee conditions who underwent PIx3-SMSx2-accelerated DL super-resolution 3T knee MRI and arthroscopy between October 2022 and December 2023 were retrospectively included. Nine fellowship-trained musculoskeletal radiologists independently scored the MRI studies for image quality and the presence of artifacts (Likert scales, range: 1 = very bad/severe, 5 = very good/absent), as well as structural abnormalities. Interreader agreements and diagnostic performance testing was performed.

resultsForty-four children (mean age: 15 ± 2 years; range: 9-17 years; 24 boys) who underwent knee MRI and arthroscopic surgery within 22 days (range, 2-133) were evaluated. Overall image quality was very good (median rating: 5 [IQR: 4-5]). Motion artifacts (5 [5-5]) and image noise (5 [4-5]) were absent. Arthroscopy-verified abnormalities were detected with good or better interreader agreement (κ ≥ 0.74). Sensitivity, specificity, accuracy, and AUC values were 100%, 84%, 93%, and 0.92, respectively, for anterior cruciate ligament tears; 71%, 97%, 93%, and 0.84 for medial meniscus tears; 65%, 100%, 86%, and 0.82 for lateral meniscus tears; 100%, 100%, 100%, and 1.00 for discoid lateral menisci; 100%, 95%, 96%, and 0.98 for medial patellofemoral ligament tears; and 55%, 100%, 98%, and 0.77 for articular cartilage defects.

conclusionClinical sub-5-min PIx3-SMSx2-accelerated DL super-resolution 3T knee MRI provides excellent image quality and high diagnostic performance for diagnosing internal derangement in children and adolescents.

Indexed as

ArthroscopyDeep LearningKnee InjuriesKnee JointMagnetic Resonance ImagingAdolescentChildFemaleHumansMaleReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityDeep learningDiagnostic performanceParallel imagingPediatric knee MRISimultaneous multislice

Identifiers

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.