Automatic context-based health informatics system for diagnosing spinal deformity using deep learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17572-3.pdf
Reference27 articles.
1. Ayhan S, Yuksel S, Nabiyev V, Adhikari P, Villa-Casademunt A, Pellise F, European Spine Study Group (2018) The influence of diagnosis, age, and gender on surgical outcomes in patients with adult spinal deformity. Global Spine J 8(8):803–809
2. Kim J, Arvind S, Oermann V, Kaji EK, Ranson D, Ukogu W, C. & Cho, S. K. (2018) Predicting surgical complications in patients undergoing elective adult spinal deformity procedures using machine learning. Spine Deformity 6(6):762–770
3. Bao H, Liu Z, Zhang Y, Sun X, Jiang J, Qian B, Zhu Z (2019) Sequential correction technique to avoid postoperative global coronal decompensation in rigid adult spinal deformity: a technical note and preliminary results. Eur Spine J 28(9):2179–2186
4. Dufvenberg M, Diarbakerli E, Charalampidis A, Öberg B, Tropp H, Aspberg Ahl A, Abbott A (2021) Six-Month Results on Treatment Adherence, Physical Activity, Spinal Appearance, Spinal Deformity, and Quality of Life in an Ongoing Randomised Trial on Conservative Treatment for Adolescent Idiopathic Scoliosis (CONTRAIS). J Clin Med 10(21):4967
5. Bari T, Ohrt-Nissen J, Hansen S, Dahl LVB, Gehrchen M (2019) The ability of the global alignment and proportion score to predict mechanical failure following adult spinal deformity surgery—validation in 149 patients with two-year follow-up. Spine Deformity 7(2):331–337
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