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Deep learning role in scoliosis detection and treatment

Scoliosis is a common skeletal condition in which a curvature forms along the coronal plane of the spine. Although scoliosis has been long recognized, its pathophysiology and best mode of treatment are still debated. Currently, definitive diagnosis of scoliosis and its progression are performed through anterior-posterior (AP) radiographs by measuring the angle of coronal curvature, referred to as Cobb angle. Cobb angle measurements can be performed by Deep Learning algorithms and are currently being investigated as a possible diagnostic tool for clinicians. This thesis focuses on the role of Deep Learning in the diagnosis and treatment of Scoliosis and proposes a study design using the algorithms to continue to better understand and classify the disease.

Identiferoai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/47975
Date29 January 2024
CreatorsGuanche, Luis
ContributorsSmith, Michael, Weinstein, John
Source SetsBoston University
Languageen_US
Detected LanguageEnglish
TypeThesis/Dissertation
RightsAttribution 4.0 International, http://creativecommons.org/licenses/by/4.0/

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