Evidence map›Paper›PMID 40293511›Full record

ArticleInternational orthopaedics2025

Deep learning-assisted detection of meniscus and anterior cruciate ligament combined tears in adult knee magnetic resonance imaging: a crossover study with arthroscopy correlation.

Julien Behr, Christophe Nich, Gaspard D'Assignies, Catalin Zavastin, Pascal Zille, Guillaume Herpe, Ramy Triki, Charles Grob, Nicolas Pujol

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Article in International orthopaedics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

9 authors.

Julien BehrNantes Université, CHU Nantes, Clinique Chirurgicale Orthopédique et Traumatologique, Nantes, France.
Christophe NichNantes Université, CHU Nantes, Clinique Chirurgicale Orthopédique et Traumatologique, Nantes, France. pr.christophe.nich@gmail.com.
Gaspard D'AssigniesIncepto Medical, Paris, France.
Catalin ZavastinUniversité Versailles Saint-Quentin-en-Yvelines, Centre hospitalier de Versailles - Hôpital Mignot, Service de Radiologie, Versailles, France.
Pascal ZilleIncepto Medical, Paris, France.
Guillaume HerpeIncepto Medical, Paris, France.
Ramy TrikiNantes Université, CHU Nantes, Clinique Chirurgicale Orthopédique et Traumatologique, Nantes, France.
Charles GrobUniversité Versailles Saint-Quentin-en-Yvelines, Centre hospitalier de Versailles - Hôpital Mignot, Service de Chirurgie Orthopédique et Traumatologique, Versailles, France.
Nicolas PujolUniversité Versailles Saint-Quentin-en-Yvelines, Centre hospitalier de Versailles - Hôpital Mignot, Service de Chirurgie Orthopédique et Traumatologique, Versailles, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimWe aimed to compare the diagnostic performance of physicians in the detection of arthroscopically confirmed meniscus and anterior cruciate ligament (ACL) tears on knee magnetic resonance imaging (MRI), with and without assistance from a deep learning (DL) model.

methodsWe obtained preoperative MR images from 88 knees of patients who underwent arthroscopic meniscal repair, with or without ACL reconstruction. Ninety-eight MR images of knees without signs of meniscus or ACL tears were obtained from a publicly available database after matching on age and ACL status (normal or torn), resulting in a global dataset of 186 MRI examinations. The Keros

resultsThe Keros

conclusionsThe current model consistently helped physicians in the detection of medial meniscus and ACL tears, notably when they were combined. LEVEL OF EVIDENCE: Diagnostic study, Level III.

Indexed as

Anterior Cruciate Ligament InjuriesArthroscopyDeep LearningMagnetic Resonance ImagingTibial Meniscus InjuriesAdultAnterior Cruciate Ligament ReconstructionCross-Over StudiesFemaleHumansMaleMiddle AgedSensitivity and SpecificityYoung AdultAIAnterior cruciate ligamentDeep learningKneeMachine learningMeniscusMRI

Identifiers

PMID40293511

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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.