Application of deep learning in analysing morphological parameters of cervical computed tomography scans-Reference-Cited by-同舟云学术

Application of deep learning in analysing morphological parameters of cervical computed tomography scans

Published:2024-02-16 Issue:1 Volume:7 Page:50-57
ISSN:2520-8985
Container-title:Chinese Journal of Academic Radiology
language:en
Short-container-title:Chin J Acad Radiol

Author:

Li Yuan,Zhang Enlong,Ouyang Hanqiang,Liu Xiaoming,Pang Huacheng,Hao Yating,Hu Daole,Dong Shuyu,Shi Xiangjun,Tian Shuai,Dong Pei,Jiang Liang,Lang Ning^ORCID,Yuan Huishu

Funder

Beijing Natural Science Foundation

National Natural Science Foundation of China

Peking University Third Hospital Clinical Key Project

Peking University Third Hospital Clinical Cohort Construction Project

Peking University Health Science Center Education and Teaching Research Project

Peking University Teaching and Learning 2.0

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42058-024-00136-1.pdf

Reference24 articles.

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2. Toki S, Higashino K, Manabe H, et al (2021) Morphometric analysis of subaxial cervical spine with myelopathy: a comparison with the normal population. Spine Surg Relat Res 5:34–40.https://doi.org/10.22603/ssrr.2020-0061

3. Pavlov H, Torg JS, Robie B, Jahre C. Cervical spinal stenosis: determination with vertebral body ratio method. Radiology. 1987;164:771–5. https://doi.org/10.1148/radiology.164.3.3615879.

4. Aebli N, Wicki AG, Rüegg TB, et al. The Torg-Pavlov ratio for the prediction of acute spinal cord injury after a minor trauma to the cervical spine. Spine J. 2013;13:605–12. https://doi.org/10.1016/j.spinee.2012.10.039.

5. Kumar A, Sahu S, Sethi S, et al. Computerized tomography-based morphometric analysis of cervical spinal canal in central indian population. J Neurosci Rural Pract. 2020;11:274–7. https://doi.org/10.1055/s-0040-1703962.