Article type
Year
Abstract
Background:
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. Anterior cruciate ligament (ACL) and meniscal injuries are two common knee sports injuries. Concomitant meniscal lesions are common in patients with anterior cruciate ligament (ACL) injuries and frequently involve the posterior horn. Although many machine learning algorithms have been developed to detect ACL and meniscal injuries based on MRI, the performance of different algorithms required further investigation.
Objectives:
To evaluate the diagnostic accuracy of machine-learning-assisted detection for ACL and meniscal injuries based on MRI and find the current best algorithm.
Methods:
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 (QUADAS-2). Clinical diagnostic tests exploring the efficacy of machine-learning-assisted system for detecting ACL and meniscal injuries based on MRI will be considered for inclusion. Another two 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 and meniscal injuries 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 applicable, we will conduct subgroup based on pre-set criteria to find more information: (1) different type and degree of ACL and meniscal injuries; (2) different machine learning algorithms used in primary studies; (3) different MRI sequences and magnet intensities used in primary studies. If the pooling of results is considered inappropriate, we will present and describe our findings in diagrams and tables and describe them narratively.
Results:
Conclusions:
Patient or healthcare consumer involvement:
There was no patient or healthcare consumer in this project.
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. Anterior cruciate ligament (ACL) and meniscal injuries are two common knee sports injuries. Concomitant meniscal lesions are common in patients with anterior cruciate ligament (ACL) injuries and frequently involve the posterior horn. Although many machine learning algorithms have been developed to detect ACL and meniscal injuries based on MRI, the performance of different algorithms required further investigation.
Objectives:
To evaluate the diagnostic accuracy of machine-learning-assisted detection for ACL and meniscal injuries based on MRI and find the current best algorithm.
Methods:
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 (QUADAS-2). Clinical diagnostic tests exploring the efficacy of machine-learning-assisted system for detecting ACL and meniscal injuries based on MRI will be considered for inclusion. Another two 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 and meniscal injuries 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 applicable, we will conduct subgroup based on pre-set criteria to find more information: (1) different type and degree of ACL and meniscal injuries; (2) different machine learning algorithms used in primary studies; (3) different MRI sequences and magnet intensities used in primary studies. If the pooling of results is considered inappropriate, we will present and describe our findings in diagrams and tables and describe them narratively.
Results:
Conclusions:
Patient or healthcare consumer involvement:
There was no patient or healthcare consumer in this project.