Physical tests for diagnosing anterior cruciate ligament rupture

Kopkow, Christian; Lange, Toni; Hoyer, Annika; Lützner, Jörg; Schmitt, Jochen

doi:10.1002/14651858.cd011925

Cited by 5 publications

(10 citation statements)

References 40 publications

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“…These are data from the screening process of two already published reviews (R‐1, 9‐11 R‐3 11 ) of diagnostic studies in the field of orthopedics and physical therapy. The review R‐2 12 contains data of an unpublished Cochrane review. The sizes are typical for diagnostic test systematic reviews in the field of medicine.…”

Section: Methodsmentioning

confidence: 99%

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies

Lange

Schwarzer

Datzmann

et al. 2021

Research Synthesis Methods

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Updating systematic reviews is often a time‐consuming process that involves a lot of human effort and is therefore not conducted as often as it should be. The aim of our research project was to explore the potential of machine learning methods to reduce human workload. Furthermore, we evaluated the performance of deep learning methods in comparison to more established machine learning methods. We used three available reviews of diagnostic test studies as the data set. In order to identify relevant publications, we used typical text pre‐processing methods. The reference standard for the evaluation was the human‐consensus based on binary classification (inclusion, exclusion). For the evaluation of the models, various scenarios were generated using a grid of combinations of data preprocessing steps. Moreover, we evaluated each machine learning approach with an approach‐specific predefined grid of tuning parameters using the Brier score metric. The best performance was obtained with an ensemble method for two of the reviews, and by a deep learning approach for the other review. Yet, the final performance of approaches strongly depends on data preparation. Overall, machine learning methods provided reasonable classification. It seems possible to reduce human workload in updating systematic reviews by using machine learning methods. Yet, as the influence of data preprocessing on the final performance seems to be at least as important as choosing the specific machine learning approach, users should not blindly expect a good performance by solely using approaches from a popular class, such as deep learning.

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Section: Methodsmentioning

confidence: 99%

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies

Lange

Schwarzer

Datzmann

et al. 2021

Research Synthesis Methods

Self Cite

View full text Add to dashboard Cite

show abstract

“…These are data from the screening process of two already published reviews (R-1 8-10 , R-3 10 ) of diagnostic studies in the field of orthopaedics and physical therapy. The review R-2 11 contains data of an unpublished Cochrane review. The size of the review is typical for diagnostic test systematic reviews in the field of medicine.…”

Section: Methodsmentioning

confidence: 99%

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies

Lange

Schwarzer

Datzmann

et al. 2020

Preprint

Self Cite

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Background: Updating systematic reviews is often a time-consuming process involving a lot of human effort and is therefore not carried out as often as it should be. Our aim was therefore to explore the potential of machine learning methods to reduce the human workload, and to particularly also gauge the performance of deep learning methods as compared to more established machine learning methods. Methods: We used three available reviews of diagnostic test studies as data basis. In order to identify relevant publications we used typical text pre-processing methods. The reference standard for the evaluation was the human-consensus based binary classification (inclusion, exclusion). For the evaluation of models various scenarios were generated using a grid of combinations of data preprocessing steps. Furthermore, we evaluated each machine learning approach with an approach-specific predefined grid of tuning parameters using the Brier score metric. Results: The best performance was obtained with an ensemble method for two of the reviews, and by a deep learning approach for the other review. Yet, the final performance of approaches is seen to strongly depend on data preparation. Overall, machine learning methods provided reasonable classification. Conclusion: It seems possible to reduce the human workload in updating systematic reviews by using machine learning methods. Yet, as the influence of data preprocessing on the final performance seems to be at least as important as choosing the specific machine learning approach, users should not blindly expect good performance just by using approaches from a popular class, such as deep learning.

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“…It is a time-consuming process to interpret knee ACL injuries, tears in meniscus, knee cartilages abnormalities which causes knee osteoarthritis, osteoporosis and knee joint replacement from radiology images manually [ 5 ]. There are many methods to diagnose an ACL tear in the knee: physical tests, and biomarkers [ 6 ], X-ray, computed tomography (CT), mammography, ultrasound imaging and magnetic resonance imaging (MRI) [ 7 ]. MRI is the best choice for diagnosing ACL tears as ACL is not visible as a plain file X-ray [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

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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Physical tests for diagnosing anterior cruciate ligament rupture

Cited by 5 publications

References 40 publications

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies

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

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