The accuracy of MRI in the diagnosis of anterior cruciate ligament injury

Zhao, Mengou; Zhou, Ying; Chang, Junru; Hu, Jie; Liu, Huixia; Wang, Shuhang; Si, Donglei; Yang, Yuchong; Li, Haiyan

doi:10.21037/atm-20-7391

Cited by 32 publications

(39 citation statements)

References 21 publications

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“…It showed that the MRI images under the reconstruction algorithm can effectively improve the diagnosis effect of knee joint epiphyseal injury. Zhao et al (2020) [29] mentioned the high resolution of MRI in the study, which can clearly show the damage of the meniscus and ligament of the patient and can classify the damage. is was consistent with the opinion of this article on the MRI results of meniscal injury patients, that is, MRI can clearly diagnose the surrounding soft tissue injury, showing soft tissue swelling, soft tissue gaps are blurred, and the soft tissue was patchy with low T1WI and high FS-PDWI signals.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Magnetic Resonance Image Feature Analysis under Reconstruction Algorithm for Knee Epiphyseal Injury

Tang

Zhou

et al. 2022

Scientific Programming

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To analyze the diagnostic value of magnetic resonance imaging (MRI) in epiphyseal injury of adolescent children, MRI images of 26 adolescent knee joint injuries were selected. The image display before and after reconstruction based on the compressed sensing theory (CS) algorithm was compared and analyzed, so did the signal-to-noise ratio of knee MRI reconstructed images under 2D random sampling and radial sampling at different sampling rates. The results showed that the sharpness, specificity, and accuracy of images reconstructed by the CS algorithm were significantly higher than those before reconstruction P < 0.05 . When the sampling rates were almost the same (20%, 40%, and 60%), the signal-to-noise ratio (SNR) of the reconstructed knee MRI images by random sampling was higher than that by radial sampling (about 9%, 6%, and 3% higher, respectively), that is, the information contained by random sampling was larger than that by radial sampling. It overcame the defects of undisplaced epiphyseal injuries in children and adolescents that cannot be found by plain X-ray films and can clearly and accurately diagnose epiphyseal injuries and fracture types that cannot be seen by plain X-ray films. To sum up, the MRI image reconstruction algorithm based on compressed sensing theory can effectively improve the diagnosis effect of knee epiphyseal injury.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Magnetic Resonance Image Feature Analysis under Reconstruction Algorithm for Knee Epiphyseal Injury

Tang

Zhou

et al. 2022

Scientific Programming

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“…This has prompted increasing attention toward the SB technique and the anatomical structure of the ACL. Knee magnetic resonance imaging (MRI) can re ect the basic physiological information of the ACL well 11 , and thus contralateral knee joint MRI examinations have become an important part of personalized ACL reconstructions. MRI analysis of normal knee joints has shown its capability to provide an anatomical basis for the ACL reconstruction procedure, including ACL size and insertion area, the positional relationship with the surrounding anatomical landmarks with different motions, and the size requirement of the autograft.…”

Section: Introductionmentioning

confidence: 99%

Transverse drill lead angle and positioning during tibial tunnel creation in the anatomical single-bundle reconstruction of the anterior cruciate ligament

Yan

Fan

et al. 2022

Preprint

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Objective To determine the optimal tibial insertion orientation for guiding the transverse drill lead angle and tibial positioner during reconstruction of a single-bundle anterior cruciate ligament (ACL). Methods A total of 48 patients (26 males; 22 females) with normal knee magnetic resonance imaging (MRI) were screened. Ultimately, 46 patients underwent bilateral knee MRI examination. Results For right vs the left knees, the tibial ACL insertion area was 120.10±5.40 mm2 and 119.46±6.59 mm2, respectively in males, and 114.76±7.01 mm2 and 113.68±8.30 mm2, respectively, in females. The angle between the long axis of the tibial ALC insertion area and the anteroposterior line of the tibial plateau was 20.47±3.91° and 20.95 ± 4.15°, respectively, in males, and 19.96±4.00° and 20.60±4.54°, respectively, in females. Additionally, the angle formed by the ACL and the tibial plateau in the sagittal plane was 43.82±4.56° and 44.76±3.58° respectively, in males, and 45.20±3.43° and 44.46±2.17°, respectively, in females. The distance between the midpoint of the tibial ACL insertion and the anterior edge of the tibial plateau in the sagittal plane was 21.59±2.32 mm and 21.79±2.02 mm, respectively, in males, and 18.93±1.12 mm and 20.13±1.84 mm, respectively, in females. Similarly, the length of this point from the posterior edge of the tibial plateau was 34.56±3.36 mm and 34.54±3.04 mm, respectively, in males, and 28.85±2.18 mm and 29.03±2.18 mm, respectively, in females. The coronal angle between the ACL and the tibial plateau was 66.37±4.44° and 66.49±4.96°, respectively, in males, and 66.70±4.84° and 66.39±2.79°, respectively, in females, and the length from the midpoint of the tibial ACL insertion in the coronal plane to the inner edge of the tibial plateau was 34.28±1.70 mm and 35.90±1.62 mm, respectively in males, and was 30.21±1.59 mm and 31.84±1.71 mm respectively, in females. The length of this point from the outer edge of the tibial plateau was also 43.82±2.09 mm and 41.70±1.85 mm, respectively, in males, and 37.50±1.72 mm and 35.64±2.29 mm, respectively, in females. Finally, the width of the tibial ACL insertion in the coronal plane was 9.90±1.66 mm and 9.54±1.64 mm, respectively in males, and 8.50±1.03 mm and 8.53±1.18 mm, respectively, in females. Conclusion This study determined the optimal drill lead angle for tibia positioning during ACL reconstruction in Chinese patients.

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“… 5–7 In a study conducted by Zhao et al, 78 participants were investigated; the diagnostic sensitivity, specificity, and accuracy of MRI for ACL injury were 95.45%, 91.67%, and 94.87%. 5 …”

Section: Introductionmentioning

confidence: 99%

“…3,4 The typical MRI findings of ACL rupture include abnormal orientation and abnormal signal intensity of ACL. [5][6][7] In a study conducted by Zhao et al, 78 participants were investigated; the diagnostic sensitivity, specificity, and accuracy of MRI for ACL injury were 95.45%, 91.67%, and 94.87%. 5 Although MRI has high sensitivity and specificity, the diagnosis rate at the early stage of ACL rupture remains poor.…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7] In a study conducted by Zhao et al, 78 participants were investigated; the diagnostic sensitivity, specificity, and accuracy of MRI for ACL injury were 95.45%, 91.67%, and 94.87%. 5 Although MRI has high sensitivity and specificity, the diagnosis rate at the early stage of ACL rupture remains poor. [8][9][10] The potential causes include less experienced radiologists and insufficient awareness of physicians, especially for emergency physicians.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Application of Unsupervised Machine Learning and Supervised Machine Learning-Derived Radiomics in Anterior Cruciate Ligament Rupture

Chen¹,

Wang²,

Zhu³

et al. 2021

RMHP

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Purpose We aim to present an unsupervised machine learning application in anterior cruciate ligament (ACL) rupture and evaluate whether supervised machine learning-derived radiomics features enable prediction of ACL rupture accurately. Patients and Methods Sixty-eight patients were reviewed. Their demographic features were recorded, radiomics features were extracted, and the input dataset was defined as a collection of demographic features and radiomics features. The input dataset was automatically classified by the unsupervised machine learning algorithm. Then, we used a supervised machine learning algorithm to construct a radiomics model. The t -test and least absolute shrinkage and selection operator (LASSO) method were used for feature selection, random forest and support vector machine (SVM) were used as machine learning classifiers. For each model, the sensitivity, specificity, accuracy, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves were calculated to evaluate model performance. Results In total, 5 demographic features were recorded and 106 radiomics features were extracted. By applying the unsupervised machine learning algorithm, patients were divided into 5 groups. Group 5 had the highest incidence of ACL rupture and left knee involvement. There were significant differences in left knee involvement among the groups. Forty-three radiomics features were extracted using t -test and 7 radiomics features were extracted using LASSO method. We found that the combination of LASSO selection method and random forest classifier has the highest sensitivity, specificity, accuracy, and AUC. The 7 radiomics features extracted by LASSO method were potential predictors for ACL rupture. Conclusion We validated the clinical application of unsupervised machine learning involving ACL rupture. Moreover, we found 7 radiomics features which were potential predictors for ACL rupture. The study indicated that radiomics could be a valuable method in the prediction of ACL rupture.

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The accuracy of MRI in the diagnosis of anterior cruciate ligament injury

Cited by 32 publications

References 21 publications

Diagnostic Value of Magnetic Resonance Image Feature Analysis under Reconstruction Algorithm for Knee Epiphyseal Injury

Diagnostic Value of Magnetic Resonance Image Feature Analysis under Reconstruction Algorithm for Knee Epiphyseal Injury

Transverse drill lead angle and positioning during tibial tunnel creation in the anatomical single-bundle reconstruction of the anterior cruciate ligament

A Novel Application of Unsupervised Machine Learning and Supervised Machine Learning-Derived Radiomics in Anterior Cruciate Ligament Rupture

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