Evidence map›Paper›PMID 36607553›Full record

ArticleJapanese journal of radiology2023

A transfer learning approach for staging diagnosis of anterior cruciate ligament injury on a new modified MR dual precision positioning of thin-slice oblique sagittal FS-PDWI sequence.

Jinhua Chen, Kun Li, Xiya Peng, Lisong Li, Huilin Yang, Lixin Huang, Jun Du

Abstract read
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In one paragraph

Article in Japanese journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
3.5field-weighted citation impact, top 8% of its field
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

7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Review
  5. Review
  6. Review
  7. 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 at 1 institution in 1 country.

Jinhua Chen *Department of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215000, China.
Kun Li *Department of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215000, China.
Xiya PengDepartment of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215000, China.
Lisong LiDepartment of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215000, China.
Huilin YangDepartment of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215000, China.
Lixin HuangDepartment of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215000, China.
Jun DuDepartment of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215000, China. dujun5166@163.com.ORCID http://orcid.org/0000-0002-5023-7837
Soochow University · CN

Funding

National Natural Science Foundation of China 81701649National Natural Science Foundation of China 82102609
6 · The paper itself

Abstract

purposeTo evaluate the diagnostic performance of the transfer learning approach for grading diagnosis of ACL injury on a new modified dual precision positioning of thin-slice oblique sagittal FS-PDWI (DPP-TSO-Sag-FS-PDWI) sequence. And compare the prediction performances between artificial intelligence (AI) and radiologists. MATERIALS AND

methodsPatients with both DPP-TSO-Sag-FS-PDWI sequence and arthroscopic results were included. We performed a transfer learning approach using the pre-trained EfficientNet-B0 model, including whole image and regions of interest (ROI) image inputs, and reset its parameters to achieve an automatic hierarchical diagnosis of ACL.

resultsA total of 235 patients (145 men and 90 women, 37.91 ± 14.77 years) with 665 images were analyzed. The consistencies of AI and arthroscopy (Kappa value > 0.94), radiologists and arthroscopy (Kappa value > 0.83, p = 0.000) were almost perfect. No statistical difference exists between the whole image and radiologists in the diagnosis of normal ACL (p = 0.063) and grade 3 injury (p = 1.000), while the whole image was better than radiologists in grade 1 (p = 0.012) and grade 2 injury (p = 0.003).

conclusionThe transfer learning approach exhibits its feasibility in the diagnosis of ACL injury based on the new modified MR DPP-TSO-Sag-FS-PDWI sequence, suggesting that it can help radiologists hierarchical diagnose ACL injuries, especially grade 2 injury.

Indexed as

Anterior Cruciate Ligament InjuriesKnee InjuriesAnterior Cruciate LigamentArtificial IntelligenceFemaleHumansMachine LearningMagnetic Resonance ImagingMaleAnterior cruciate ligamentDiagnostic performanceMagnetic resonance imagingPrecision positioning of thin-slice oblique sagittal fat suppression proton density-weighted imagingTransfer learning approach

Identifiers

PMID36607553
OpenAlexW4313649627

What OpenQuestion holds

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Registered trials

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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.