Detection of Cervical Foraminal Stenosis from Oblique Radiograph Using Convolutional Neural Network Algorithm-Reference-Cited by-同舟云学术

Detection of Cervical Foraminal Stenosis from Oblique Radiograph Using Convolutional Neural Network Algorithm

Published:2024 Issue:7 Volume:65 Page:389
ISSN:0513-5796
Container-title:Yonsei Medical Journal
language:en
Short-container-title:Yonsei Med J

Author:

Kim Jihie¹^ORCID,Yang Jae Jun²^ORCID,Song Jaeha³^ORCID,Jo SeongWoon⁴^ORCID,Kim YoungHoon³^ORCID,Park Jiho¹^ORCID,Lee Jin Bog²^ORCID,Lee Gun Woo⁵^ORCID,Park Sehan⁶^ORCID

Affiliation:

1. Department of Artificial Intelligence, Dongguk University, Seoul, Korea.

2. Department of Orthopedic Surgery, Dongguk University Ilsan Hospital, Goyang, Korea.

3. Department of Computer Science and Engineering, Dongguk University, Seoul, Korea.

4. Department of Statistics, Dongguk University, Seoul, Korea.

5. Department of Orthopedic Surgery, Yeongnam University Medical Center, Yeongnam University College of Medicine, Daegu, Korea.

6. Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Funder

Ministry of Science and ICT, South Korea

Artificial Intelligence Convergence Innovation Human Resources Development

Publisher

XMLink

Link

https://eymj.org/pdf/10.3349/ymj.2023.0091

Reference30 articles.

1. Usefulness of Simultaneous Magnetic Resonance Neurography and Apparent T2 Mapping for the Diagnosis of Cervical Radiculopathy

2. Systematic review of radiological cervical foraminal grading systems

3. Foraminal Restenosis After Posterior Cervical Foraminotomy for the Treatment of Cervical Radiculopathy

4. Diagnostic Technology for Spine Pathology

5. Assessing Foraminal Stenosis in the Cervical Spine: A Comparison of Three-Dimensional Computed Tomographic Surface Reconstruction to Two-Dimensional Modalities