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Article

Computer-Assisted System in Stress Radiography for Anterior Cruciate Ligament Injury with Correspondent Evaluation of Relevant Diagnostic Factors

1
Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1 University Rd, Tainan 704, Taiwan
2
Department of Statistics, National Cheng Kung University, No. 1 University Rd, Tainan 704, Taiwan
3
Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1 University Rd, Tainan 704, Taiwan
4
Department of Medical Imaging and Radiological Sciences, I-Shou University, No.8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan
*
Authors to whom correspondence should be addressed.
Diagnostics 2021, 11(3), 419; https://doi.org/10.3390/diagnostics11030419
Submission received: 24 December 2020 / Revised: 20 February 2021 / Accepted: 26 February 2021 / Published: 2 March 2021
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

Abstract

We sought to design a computer-assisted system measuring the anterior tibial translation in stress radiography, evaluate its diagnostic performance for an anterior cruciate ligament (ACL) tear, and assess factors affecting the diagnostic accuracy. Retrospective research for patients with both knee stress radiography and magnetic resonance imaging (MRI) at our institution was performed. A complete ACL rupture was confirmed on an MRI. The anterior tibial translations with four different methods were measured in 249 patients by the designed algorithm. The diagnostic accuracy of each method in patients with all successful measurements was evaluated. Univariate logistic regression analysis for factors affecting diagnostic accuracy of method four was performed. In the inclusive 249 patients, 177 patients (129 with completely torn ACLs) were available for analysis. Mean anterior tibial translations were significantly increased in the patients with a completely torn ACL by all four methods, with diagnostic accuracies ranging from 66.7% to 75.1%. The diagnostic accuracy of method four was negatively associated with the time interval between stress radiography and MRI as well as force-joint distance on stress view, and not significantly associated with age, gender, flexion angle, intercondylar distance, and force-joint angle. A computer-assisted system measuring the anterior tibial translation in stress radiography showed acceptable diagnostic performance of complete ACL injury. A shorter time interval between stress radiography and MRI as well as shorter force-joint distance were associated with higher diagnostic accuracy.
Keywords: knee; anterior cruciate ligament; ACL; stress radiography; computer-aided diagnosis knee; anterior cruciate ligament; ACL; stress radiography; computer-aided diagnosis

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MDPI and ACS Style

Wang, C.-K.; Lin, L.-C.; Sun, Y.-N.; Lai, C.-S.; Chen, C.-H.; Kao, C.-Y. Computer-Assisted System in Stress Radiography for Anterior Cruciate Ligament Injury with Correspondent Evaluation of Relevant Diagnostic Factors. Diagnostics 2021, 11, 419. https://doi.org/10.3390/diagnostics11030419

AMA Style

Wang C-K, Lin L-C, Sun Y-N, Lai C-S, Chen C-H, Kao C-Y. Computer-Assisted System in Stress Radiography for Anterior Cruciate Ligament Injury with Correspondent Evaluation of Relevant Diagnostic Factors. Diagnostics. 2021; 11(3):419. https://doi.org/10.3390/diagnostics11030419

Chicago/Turabian Style

Wang, Chien-Kuo, Liang-Ching Lin, Yung-Nien Sun, Cheng-Shih Lai, Chia-Hui Chen, and Cheng-Yi Kao. 2021. "Computer-Assisted System in Stress Radiography for Anterior Cruciate Ligament Injury with Correspondent Evaluation of Relevant Diagnostic Factors" Diagnostics 11, no. 3: 419. https://doi.org/10.3390/diagnostics11030419

APA Style

Wang, C.-K., Lin, L.-C., Sun, Y.-N., Lai, C.-S., Chen, C.-H., & Kao, C.-Y. (2021). Computer-Assisted System in Stress Radiography for Anterior Cruciate Ligament Injury with Correspondent Evaluation of Relevant Diagnostic Factors. Diagnostics, 11(3), 419. https://doi.org/10.3390/diagnostics11030419

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