Share：
Share this content in WeChat
X
[Chinese][PDF]133128
• Review •
Development of artificial intelligence in diagnosis and treatment of spinal diseases
ZHAO Weili  ZHANG Enlong  LIU Ke  WANG Qizheng  CHEN Yongye  YUAN Huishu  LANG Ning 

Cite this article as: Zhao WL, Zhang EL, Liu K, et al. Development of artificial intelligence in diagnosis and treatment of spinal diseases[J]. Chin J Magn Reson Imaging, 2022, 13(6): 160-163. DOI:10.12015/issn.1674-8034.2022.06.034.


[Abstract] Artificial intelligence mainly refers to machine learning, and deep learning is a specific type of mechine learning. The technologies in the field of artificial intelligence, especially the deep learning methods, have been widely used in medical image and big data processing, including image reconstruction, image processing, image analysis and model construction. By using the related methods of artificial intelligence, we can achieve the aim of location and segmentation of spinal structure, and the comprehensive analysis of spinal diseases, such as the diagnosis and differential diagnosis, clinical decision support and prognosis prediction, which provides the basis for the selection of the most reasonable treatment of spinal diseases.
[Keywords] artificial intelligence；deep learning；spine；vertebral fracture；spinal degenerative diseases；spinal tumor；spine deformity；diagnosis；treatment；prognosis prediction

ZHAO Weili1   ZHANG Enlong2   LIU Ke1   WANG Qizheng1   CHEN Yongye1   YUAN Huishu1   LANG Ning1*  

1 Department of Radiology, Peking University Third Hospital, Beijing 100191, China

2 Department of Radiology, Peking University International Hospital, Beijing 102206, China

Lang N, E-mail: 13501241339@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81971578); Natural Science Foundation of Beijing (No. Z190020).
Received  2022-03-11
Accepted  2022-04-24
DOI: 10.12015/issn.1674-8034.2022.06.034
Cite this article as: Zhao WL, Zhang EL, Liu K, et al. Development of artificial intelligence in diagnosis and treatment of spinal diseases[J]. Chin J Magn Reson Imaging, 2022, 13(6): 160-163. DOI:10.12015/issn.1674-8034.2022.06.034.

[1]
Yasaka K, Akai H, Kunimatsu A, et al. Deep learning with convolutional neural network in radiology[J]. Jpn J Radiol, 2018, 36(4): 257-272. DOI: 10.1007/s11604-018-0726-3.
[2]
Murata K, Endo K, Aihara T, et al. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography[J]. Sci Rep, 2020, 10(1): 20031. DOI: 10.1038/s41598-020-76866-w.
[3]
Ulivieri FM, Rinaudo L, Piodi LP, et al. Bone strain index as a predictor of further vertebral fracture in osteoporotic women: an artificial intelligence-based analysis[J]. PLoS One, 2021, 16(2): e0245967. DOI: 10.1371/journal.pone.0245967.
[4]
Li Y, Zhang Y, Zhang EL, et al. Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning[J]. Eur Radiol, 2021, 31(12): 9612-9619. DOI: 10.1007/s00330-021-08014-5.
[5]
Auloge P, Cazzato RL, Ramamurthy N, et al. Augmented reality and artificial intelligence-based navigation during percutaneous vertebroplasty: a pilot randomised clinical trial[J]. Eur Spine J, 2020, 29(7): 1580-1589. DOI: 10.1007/s00586-019-06054-6.
[6]
Tian W. The epidemiological status and the development of diagnosis and treatment of spinal and joint degenerative diseases[J]. J Clin Orthop Res, 2016, 1(1): 1-3. DOI: 10.19548/j.2096-269x.2016.01.002.
[7]
Zhou YJ, Liu Y, Chen Q, et al. Automatic lumbar MRI detection and identification based on deep learning[J]. J Digit Imaging, 2019, 32(3): 513-520. DOI: 10.1007/s10278-018-0130-7.
[8]
Li XM, Dou Q, Chen H, et al. 3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images[J]. Med Image Anal, 2018, 45: 41-54. DOI: 10.1016/j.media.2018.01.004.
[9]
Gao F, Liu S, Zhang XD, et al. Automated grading of lumbar disc degeneration using a push-pull regularization network based on MRI[J]. J Magn Reson Imaging, 2021, 53(3): 799-806. DOI: 10.1002/jmri.27400.
[10]
Fan GX, Liu HQ, Wang DD, et al. Deep learning-based lumbosacral reconstruction for difficulty prediction of percutaneous endoscopic transforaminal discectomy at L5/S1 level: a retrospective cohort study[J]. Int J Surg, 2020, 82: 162-169. DOI: 10.1016/j.ijsu.2020.08.036.
[11]
Harada GK, Siyaji ZK, Mallow GM, et al. Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the "RAD" risk profile[J]. Eur Spine J, 2021, 30(8): 2167-2175. DOI: 10.1007/s00586-021-06866-5.
[12]
Li H, Luo H, Huan W, et al. Automatic lumbar spinal MRI image segmentation with a multi-scale attention network[J]. Neural Comput Appl, 2021: 1-14. DOI: 10.1007/s00521-021-05856-4.
[13]
Won D, Lee HJ, Lee SJ, et al. Spinal Stenosis grading in magnetic resonance imaging using deep convolutional neural networks[J]. Spine, 2020, 45(12): 804-812. DOI: 10.1097/BRS.0000000000003377.
[14]
Hallinan JTPD, Zhu L, Yang KY, et al. Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal Stenosis at lumbar spine MRI[J]. Radiology, 2021, 300(1): 130-138. DOI: 10.1148/radiol.2021204289.
[15]
Ghogawala Z, Dunbar M, Essa I. Artificial intelligence for the treatment of lumbar spondylolisthesis[J]. Neurosurg Clin N Am, 2019, 30(3): 383-389. DOI: 10.1016/j.nec.2019.02.012.
[16]
Wang SQ, Hu Y, Shen YY, et al. Classification of diffusion tensor metrics for the diagnosis of a myelopathic cord using machine learning[J]. Int J Neural Syst, 2018, 28(2): 1750036. DOI: 10.1142/S0129065717500368.
[17]
Jin RC, Luk KD, Cheung JPY, et al. Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods[J]. NMR Biomed, 2019, 32(8): e4114. DOI: 10.1002/nbm.4114.
[18]
Khan O, Badhiwala JH, Akbar MA, et al. Prediction of worse functional status after surgery for degenerative cervical myelopathy: a machine learning approach[J]. Neurosurgery, 2021, 88(3): 584-591. DOI: 10.1093/neuros/nyaa477.
[19]
Zhang MZ, Ou-Yang HQ, Jiang L, et al. Optimal machine learning methods for radiomic prediction models: clinical application for preoperative T2*-weighted images of cervical spondylotic myelopathy[J]. JOR Spine, 2021, 4(4): e1178. DOI: 10.1002/jsp2.1178.
[20]
Chianca V, Cuocolo R, Gitto S, et al. Radiomic machine learning classifiers in spine bone tumors: a multi-software, multi-scanner study[J]. Eur J Radiol, 2021, 137: 109586. DOI: 10.1016/j.ejrad.2021.109586.
[21]
Wang QZ, Zhang Y, Zhang EL, et al. Prediction of the early recurrence in spinal giant cell tumor of bone using radiomics of preoperative CT: long-term outcome of 62 consecutive patients[J]. J Bone Oncol, 2021, 27: 100354. DOI: 10.1016/j.jbo.2021.100354.
[22]
Karhade AV, Thio Q, Ogink P, et al. Development of machine learning algorithms for prediction of 5-year spinal chordoma survival[J]. World Neurosurg, 2018, 119: e842-e847. DOI: 10.1016/j.wneu.2018.07.276.
[23]
Ryu SM, Seo SW, Lee SH. Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks[J]. BMC Med Inform Decis Mak, 2020, 20(1): 3. DOI: 10.1186/s12911-019-1008-4.
[24]
Wang J, Fang ZY, Lang N, et al. A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks[J]. Comput Biol Med, 2017, 84: 137-146. DOI: 10.1016/j.compbiomed.2017.03.024.
[25]
Liu WC, Li ZQ, Luo ZW, et al. Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer[J]. Cancer Med, 2021, 10(8): 2802-2811. DOI: 10.1002/cam4.3776.
[26]
Lang N, Zhang Y, Zhang EL, et al. Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI[J]. Magn Reson Imaging, 2019, 64: 4-12. DOI: 10.1016/j.mri.2019.02.013.
[27]
Huang ZH, Hu C, Chi CX, et al. An artificial intelligence model for predicting 1-year survival of bone metastases in non-small-cell lung cancer patients based on XGBoost algorithm[J]. Biomed Res Int, 2020: 3462363. DOI: 10.1155/2020/3462363.
[28]
Serratrice N, Faddoul J, Tarabay B, et al. Ten years after SINS: role of surgery and radiotherapy in the management of patients with vertebral metastases[J]. Front Oncol, 2022, 12: 802595. DOI: 10.3389/fonc.2022.802595.
[29]
Massaad E, Williams N, Hadzipasic M, et al. Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions[J]. Neurosurg Focus, 2021, 50(5): E5. DOI: 10.3171/2021.2.FOCUS201113.
[30]
Arends SRS, Savenije MHF, Eppinga WSC, et al. Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases[J]. Phys Imaging Radiat Oncol, 2022, 21: 42-47. DOI: 10.1016/j.phro.2022.02.003.
[31]
Wu HB, Bailey C, Rasoulinejad P, et al. Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net[J]. Med Image Anal, 2018, 48: 1-11. DOI: 10.1016/j.media.2018.05.005.
[32]
Pan YL, Chen QR, Chen TT, et al. Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays[J]. Eur Spine J, 2019, 28(12): 3035-3043. DOI: 10.1007/s00586-019-06115-w.
[33]
Ha AY, Do BH, Bartret AL, et al. Automating scoliosis measurements in radiographic studies with machine learning: comparing artificial intelligence and clinical reports[J]. J Digit Imaging, 2022, 35(3): 524-533. DOI: 10.1007/s10278-022-00595-x.
[34]
Durand WM, Daniels AH, Hamilton DK, et al. Artificial intelligence models predict operative versus nonoperative management of patients with adult spinal deformity with 86% accuracy[J]. World Neurosurg, 2020, 141: e239-e253. DOI: 10.1016/j.wneu.2020.05.099.
[35]
Ames CP, Smith JS, Pellisé F, et al. Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value[J]. Spine (Phila Pa 1976), 2019, 44(13): 915-926. DOI: 10.1097/BRS.0000000000002974.
[36]
Hornung AL, Hornung CM, Mallow GM, et al. Artificial intelligence and spine imaging: limitations, regulatory issues and future direction[J/OL]. Eur Spine J, 2022 [2022-3-11]. https://link.springer.com/article/10.1007/s00586-021-07108-4. DOI: 10.1007/s00586-021-07108-4.

PREV The application progress of artificial intelligence in gastric cancer imaging
NEXT Application progress of whole body-magnetic resonance imaging in common tumors and screening progress in high-risk groups
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn