Diagnostic accuracy of machine-learning-assisted detection for anterior cruciate ligament injury based on magnetic resonance imaging

Abstract

Background: Although many machine learning algorithms have been developed to detect anterior cruciate ligament (ACL) injury based on magnetic resonance imaging (MRI), the performance of different algorithms required further investigation. The objectives of this current systematic review are to evaluate the diagnostic accuracy of machine-learning-assisted detection for ACL injury based on MRI and find the current best algorithm. Method: We will conduct a comprehensive database search for clinical diagnostic tests in PubMed, EMBASE, Cochrane Library, and Web of science without restrictions on publication status and language. The reference lists of the included articles will also be checked to identify additional studies for potential inclusion. Two reviewers will independently review all literature for inclusion and assess their methodological quality using Quality Assessment of Diagnostic Accuracy Studies version 2. Clinical diagnostic tests exploring the efficacy of machine-learning-assisted system for detecting ACL injury based on MRI will be considered for inclusion. Another 2 reviewers will independently extract data from eligible studies based on a pre-designed standardized form. Any disagreements will be resolved by consensus. RevMan 5.3 and Stata SE 12.0 software will be used for data synthesis. If appropriate, we will calculate the summary sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of machine-learning-assisted diagnosis system for ACL injury detection. A hierarchical summary receiver operating characteristic (HSROC) curve will also be plotted, and the area under the ROC curve (AUC) is going to calculated using the bivariate model. If the pooling of results is considered inappropriate, we will present and describe our findings in diagrams and tables and describe them narratively. Result: This is the first systematic assessment of machine learning system for the detection of ACL injury based on MRI. We predict it will provide highquality synthesis of existing evidence for the diagnostic accuracy of machine-learning-assisted detection for ACL injury and a relatively comprehensive reference for clinical practice and development of interdisciplinary field of artificial intelligence and medicine. Conclusion: This protocol outlined the significance and methodologically details of a systematic review of machine-learning-assisted detection for ACL injury based on MRI. The ongoing systematic review will provide high-quality synthesis of current evidence of machine learning system for detecting ACL injury. Registration: The meta-analysis has been prospectively registered in PROSPERO (CRD42019136581).

Sports Injury Modeling of the Anterior Cruciate Ligament Based on the Intelligent Finite Element Algorithm

“…More and more people are aware of its importance and related diseases, and their research is getting deeper and deeper. According to the analysis of 2725 cases of sports injuries in various sports events by the Institute of Sports Medicine, knee injuries accounted for 25.82%, accounting for the first of all sports injuries [ 1 ]. Different types and degrees of knee injuries have seriously affected athletes' training and sports performance.…”

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

“…The performance of ACL tear was measured by the AUC for the injury-detection 0.894 problem and for full rupture case 0.943 after being coupled with both features and machine learning support vector machines (SVM) and random forest (RF). There are various surveys, meta-analyses and reviews [ 24 , 25 ] related to anterior cruciate ligament knee injury detection through various machine learning models. It has been shown that the accuracy remained good in the case of a smaller dataset, but in the case of more radiology images, the machine learning models have not been a solution.…”

Machine Learning in Healthcare

“…Given the digital nature of data and the presence of structured data formats such as DICOM (Digital Imaging and Communications in Medicine), medical imaging has seen significant strides with the implementation of machine learning-based approaches to several imaging modalities, including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), X-Ray, Positron Emission Tomography (PET), Ultrasound, and more. Several ML-based models have been developed to identify tumors [ 42 , 43 ], lesions [ 44 ], fractures [ 45 , 46 ], and tears [ 47 , 48 ].…”