Machine learning in orthopaedic surgery

doi:10.5312/wjo.v12.i9.685

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Sep 18, 2021 (publication date) through Sep 18, 2024

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World Journal of Orthopedics

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2218-5836

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Orthop. Sep 18, 2021; 12(9): 685-699
Published online Sep 18, 2021. doi: 10.5312/wjo.v12.i9.685

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Figure 3 Input processing pipeline of T2 sagittal magnetic resonance imaging and output predictions of radiological features[65]. Reused with permission. Citation: Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battié MC, Fairbank J, McCall I; Genodisc Consortium. ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur Spine J 2017; 26: 1374-1383.

Citation: Lalehzarian SP, Gowd AK, Liu JN. Machine learning in orthopaedic surgery. World J Orthop 2021; 12(9): 685-699
URL: https://www.wjgnet.com/2218-5836/full/v12/i9/685.htm
DOI: https://dx.doi.org/10.5312/wjo.v12.i9.685