Anterior cruciate ligament (ACL) injury classification system using support vector machine (SVM)

Mazlan, S. S.; Ayob, Mohd Zaki; Bakti, Z. A. Kadir

doi:10.1109/ice2t.2017.8215960

Cited by 10 publications

(11 citation statements)

References 7 publications

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“…BP-ANN achieved a classification accuracy of 94.44% whereas K-NN reached an accuracy of 87.33%. Another study [ 51 ] tested an SVM algorithm on a dataset that was comprised of 100 non-injured ACLs, 100 partially-torn ACLs, and 100 completely-torn ACLs. All datasets underwent pre-processing.…”

Section: Resultsmentioning

confidence: 99%

“…Other pre-trained networks that were used at least once in this survey are: DenseNet [ 32 ], Le-Net [ 68 ], ImageNet [ 33 ], and R-CNN [ 41 ]. In five [ 48 , 50 , 51 , 59 , 60 ] out of the 22 studies of the present survey, more traditional ML pipelines were applied, including a separate feature engineering step (where features were manually extracted from images). SVM classification was the preferred classifier in most of the cases.…”

Section: Discussionmentioning

confidence: 99%

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Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Siouras

Moustakidis²,

Giannakidis

et al. 2022

Diagnostics

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The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Siouras

Moustakidis²,

Giannakidis

et al. 2022

Diagnostics

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“…Numerous researchers are working at their best using machine learning and deep learning techniques to identify the disease through MR images in better and novel ways. The study [ 21 ] has shown good results, after using support vector machines on 300 MR images of healthy, partial and fully ruptured ACL tears. The study was classified the human articular cartilage OARSI-scored with machine learning pattern recognition and multivariable regression techniques.…”

Section: Related Workmentioning

confidence: 99%

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

et al. 2021

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The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.

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“…Taking into account the above-reported existing methodologies and their limitations, as well the recent advances in artificial intelligence (AI), the application of machine-learning algorithms appears to be a promising approach for diagnosis and prediction in several fields, such as ACL injury [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ], and, more generally, in biomedicine approaches [ 31 , 32 , 33 ]. Focusing on ACL, diagnosis and prediction represent two correlated analyses that permit us to solve two different issues.…”

Section: Introductionmentioning

confidence: 99%

“…Considering diagnostic aspects, Lao et al provided an overview on the application of AI on magnetic resonance imaging (MRI) able to detect the ACL injury [ 27 ], among others. The study proposed by Mazlan et al showed the support vector machines were able to classify up to 100% of three different ACL injuries, which were normal, partial and crucial [ 24 ]. Similar results were obtained by applying a convolutional neural network on coronal MRI, reaching an accuracy of 96% in ACL diagnoses [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players

Taborri

Molinaro

Santospagnuolo

et al. 2021

Sensors

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Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with the clinical scale is still an open question. We proposed a machine-learning approach to accomplish this purpose. Thirty-nine female basketball players were enrolled in the study. Leg stability, leg mobility and capability to absorb the load after jump were evaluated through inertial sensors and optoelectronic bars. The risk level of athletes was computed by the Landing Error Score System (LESS). A comparative analysis among nine classifiers was performed by assessing the accuracy, F1-score and goodness. Five out nine examined classifiers reached optimum performance, with the linear support vector machine achieving an accuracy and F1-score of 96 and 95%, respectively. The feature importance was computed, allowing us to promote the ellipse area, parameters related to the load absorption and the leg mobility as the most useful features for the prediction of anterior cruciate ligament injury risk. In addition, the ellipse area showed a strong correlation with the LESS score. The results open the possibility to use such a methodology for predicting ACL injury.

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Anterior cruciate ligament (ACL) injury classification system using support vector machine (SVM)

Cited by 10 publications

References 7 publications

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players

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