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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.

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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.

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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.

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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.

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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.

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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.

Conclusion: About

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