Evidence map›Paper›PMID 37697236›Full record

ArticleBMC medical imaging2023

Effective automatic detection of anterior cruciate ligament injury using convolutional neural network with two attention mechanism modules.

Chen Liang, Xiang Li, Yong Qin, Minglei Li, Yingkai Ma, Ren Wang, Xiangning Xu, Jinping Yu, Songcen Lv, Hao Luo

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
4.4field-weighted citation impact, top 5% 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Chen LiangDepartment of Minimally Invasive Surgery and Sports Medicine, The 2Nd Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Xiang LiDepartment of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
Yong QinDepartment of Minimally Invasive Surgery and Sports Medicine, The 2Nd Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Minglei LiDepartment of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
Yingkai MaDepartment of Minimally Invasive Surgery and Sports Medicine, The 2Nd Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Ren WangDepartment of Minimally Invasive Surgery and Sports Medicine, The 2Nd Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Xiangning XuDepartment of Minimally Invasive Surgery and Sports Medicine, The 2Nd Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Jinping YuDepartment of Minimally Invasive Surgery and Sports Medicine, The 2Nd Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Songcen LvDepartment of Minimally Invasive Surgery and Sports Medicine, The 2Nd Affiliated Hospital of Harbin Medical University, Harbin, 150001, China. lvsongcen2020@126.com.
Hao LuoDepartment of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China. hao.luo@hit.edu.cn.
Harbin Medical University · CNHarbin Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo develop a fully automated CNN detection system based on magnetic resonance imaging (MRI) for ACL injury, and to explore the feasibility of CNN for ACL injury detection on MRI images.

methodsIncluding 313 patients aged 16 - 65 years old, the raw data are 368 pieces with injured ACL and 100 pieces with intact ACL. By adding flipping, rotation, scaling and other methods to expand the data, the final data set is 630 pieces including 355 pieces of injured ACL and 275 pieces of intact ACL. Using the proposed CNN model with two attention mechanism modules, data sets are trained and tested with fivefold cross-validation.

resultsThe performance is evaluated using accuracy, precision, sensitivity, specificity and F1 score of our proposed CNN model, with results of 0.8063, 0.7741, 0.9268, 0.6509 and 0.8436. The average accuracy in the fivefold cross-validation is 0.8064. For our model, the average area under curves (AUC) for detecting injured ACL has results of 0.8886.

conclusionWe propose an effective and automatic CNN model to detect ACL injury from MRI of human knees. This model can effectively help clinicians diagnose ACL injury, improving diagnostic efficiency and reducing misdiagnosis and missed diagnosis.

Indexed as

Anterior Cruciate Ligament InjuriesAdolescentAdultAgedArea Under CurveHumansMiddle AgedNeural Networks, ComputerResearch DesignYoung AdultAnterior cruciate ligament (ACL) injuryArtificial intelligence (AI)Convolutional neural network (CNN)Magnetic resonance imaging (MRI)

Identifiers

PMID37697236
PMCPMC10494428
OpenAlexW4386621292

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.