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CONCLUSION

A fully-automated deep learning approach has comparable diagnostic performance as human readers for detecting surgically confirmed ACL tears using sagittal IW-FSE images and T2-FSE images.

Conclusion: About

REFERENCE

1.Ho, Jia Hui, et al. "Anterior cruciate ligament segmentation: Using morphological operations with active contour." Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on. IEEE, 2010.

2.Lee, Hansang, Helen Hong, and Junmo Kim. "Segmentation of anterior cruciate ligament in knee MR images using graph cuts with patient-specific shape constraints and label refinement." Computers in biology and medicine 55 (2014): 1-10.

3.Lee, Han Sang, and Helen Hong. "Anterior Cruciate Ligament Segmentation in Knee MRI with Locally-aligned Probabilistic Atlas and Iterative Graph Cuts." Journal of KIISE 42.10 (2015): 1222-1230.

4.Kim, Yoon Sang, et al. "In vivo analysis of acromioclavicular joint motion after hook plate fixation using three-dimensional computed tomography." Journal of shoulder and elbow surgery 24.7 (2015): 1106-1111.

5.Bochen Guan, et al. “Can A Machine Diagnose Knee MR Images? Automated Anterior Cruciate Ligament Tear Detection System Using Deep Learning” RSNA Annual Meeting, 2018.

6. Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

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