Knezevic NN, Candido KD, Vlaeyen JWS, Van Zundert J, Cohen SP. Low back pain. Lancet. 2021;398(10294):78–92.

PubMed  Google Scholar 

Wenig CM, Schmidt CO, Kohlmann T, Schweikert B. Costs of back pain in Germany. Eur J Pain. 2009;13(3):280–6.

PubMed  Google Scholar 

Pfirrmann CW, Metzdorf A, Zanetti M, Hodler J, Boos N. Magnetic resonance classification of lumbar intervertebral disc degeneration. Spine (Phila Pa 1976). 2001;26(17):1873–8.

CAS  PubMed  Google Scholar 

Leone A, Guglielmi G, Cassar-Pullicino VN, Bonomo L. Lumbar intervertebral instability: a review. Radiology. 2007;245(1):62–77.

PubMed  Google Scholar 

Compte R, Granville Smith I, Isaac A, Danckert N, McSweeney T, Liantis P, Williams FMK. Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis. Eur Spine J. 2023;32(11):3764–87.

PubMed  Google Scholar 

Hirschmann A, Cyriac J, Stieltjes B, Kober T, Richiardi J, Omoumi P. Artificial Intelligence in Musculoskeletal Imaging: review of current literature, challenges, and Trends. Semin Musculoskelet Radiol. 2019;23(3):304–11.

PubMed  Google Scholar 

Bousson V, Benoist N, Guetat P, Attane G, Salvat C, Perronne L. Application of artificial intelligence to imaging interpretations in the musculoskeletal area: where are we? Where are we going? Joint Bone Spine. 2023;90(1):105493.

PubMed  Google Scholar 

Galbusera F, Casaroli G, Bassani T. Artificial intelligence and machine learning in spine research. JOR Spine. 2019;2(1):e1044.

PubMed  PubMed Central  Google Scholar 

Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current applications of machine learning in spine: from clinical view. Global Spine J. 2022;12(8):1827–40.

PubMed  Google Scholar 

Hornung AL, Hornung CM, Mallow GM, Barajas JN, Espinoza Orias AA, Galbusera F, Wilke HJ, Colman M, Phillips FM, An HS. Artificial intelligence and spine imaging: limitations, regulatory issues and future direction. Eur Spine J. 2022;31(8):2007–21.

PubMed  Google Scholar 

Jamaludin A, Kadir T, Zisserman A. SpineNet: automated classification and evidence visualization in spinal MRIs. Med Image Anal. 2017;41:63–73.

PubMed  Google Scholar 

Wilson B, Gaonkar B, Yoo B, Salehi B, Attiah M, Villaroman D, Ahn C, Edwards M, Laiwalla A, Ratnaparkhi A, et al. Predicting spinal surgery candidacy from Imaging Data using machine learning. Neurosurgery. 2021;89(1):116–21.

PubMed  PubMed Central  Google Scholar 

Kim JK, Wang MX, Chang MC. Deep learning algorithm trained on lumbar magnetic resonance imaging to Predict outcomes of Transforaminal Epidural Steroid Injection for Chronic Lumbosacral Radicular Pain. Pain Physician. 2022;25(8):587–92.

PubMed  Google Scholar 

Sustersic T, Rankovic V, Milovanovic V, Kovacevic V, Rasulic L, Filipovic N. A deep learning model for automatic detection and classification of disc herniation in magnetic resonance images. IEEE J Biomed Health Inf. 2022;26(12):6036–46.

Google Scholar 

Pang S, Su Z, Leung S, Nachum IB, Chen B, Feng Q, Li S. Direct automated quantitative measurement of spine by cascade amplifier regression network with manifold regularization. Med Image Anal. 2019;55:103–15.

PubMed  Google Scholar 

Liu J, Cui Z, Desrosiers C, Lu S, Zhou Y. Grayscale self-adjusting network with weak feature enhancement for 3D lumbar anatomy segmentation. Med Image Anal. 2022;81:102567.

PubMed  Google Scholar 

Martin-Noguerol T, Onate Miranda M, Amrhein TJ, Paulano-Godino F, Xiberta P, Vilanova JC, Luna A. The role of Artificial intelligence in the assessment of the spine and spinal cord. Eur J Radiol. 2023;161:110726.

PubMed  Google Scholar 

Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160.

PubMed  PubMed Central  Google Scholar 

McInnes MDF, Moher D, Thombs BD, McGrath TA, Bossuyt PM, and the, Clifford P-DTAG, Cohen T, Deeks JF, Gatsonis JJ et al. C : Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA 2018;319(4):388–396.

Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM. Group Q-: QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36.

PubMed  Google Scholar 

Wang JC, Shu YC, Lin CY, Wu WT, Chen LR, Lo YC, Chiu HC, Ozcakar L, Chang KV. Application of deep learning algorithms in automatic sonographic localization and segmentation of the median nerve: a systematic review and meta-analysis. Artif Intell Med. 2023;137:102496.

PubMed  Google Scholar 

Li X, Dou Q, Chen H, Fu CW, Qi X, Belavý DL, Armbrecht G, Felsenberg D, Zheng G, Heng PA. 3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MR Images. Med Image Anal. 2018;45:41–54.

PubMed  Google Scholar 

Cheng YK, Lin CL, Huang YC, Lin GS, Lian ZY, Chuang CH. Accurate Intervertebral Disc Segmentation Approach based on deep learning. Diagnostics (Basel Switzerland) 2024, 14(2).

Chen T, Su Z-h, Liu Z, Wang M, Cui Z-f, Zhao L, Yang L-j, Zhang W-c, Liu X, Liu J, et al. Automated magnetic resonance image segmentation of spinal structures at the L4-5 level with deep learning: 3D Reconstruction of lumbar intervertebral foramen. Orthop Surg. 2022;14(9):2256–64.

PubMed  PubMed Central  Google Scholar 

Liu Z, Su Z, Wang M, Chen T, Cui Z, Chen X, Li S, Feng Q, Pang S, Lu H. Computerized characterization of spinal structures on MRI and clinical significance of 3D Reconstruction of Lumbosacral Intervertebral Foramen. Pain Physician. 2022;25(1):E27–.

PubMed  Google Scholar 

Bharadwaj UU, Christine M, Li S, Chou D, Pedoia V, Link TM, Chin CT, Majumdar S. Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI. Eur Radiol. 2023;33(5):3435–43.

PubMed  PubMed Central  Google Scholar 

Hess M, Allaire B, Gao KT, Tibrewala R, Inamdar G, Bharadwaj U, Chin C, Pedoia V, Bouxsein M, Anderson D, et al. Deep learning for Multi-tissue Segmentation and fully automatic personalized biomechanical models from BACPAC clinical lumbar spine MRI. Pain Med. 2023;24:S139–48.

PubMed  Google Scholar 

Das P, Pal C, Acharyya A, Chakrabarti A, Basu S. Deep neural network for automated simultaneous intervertebral disc (IVDs) identification and segmentation of multi-modal MR images. Comput Methods Programs Biomed. 2021;205:106074.

PubMed  Google Scholar 

Pang S, Pang C, Su Z, Lin L, Zhao L, Chen Y, Zhou Y, Lu H, Feng Q. DGMSNet: spine segmentation for MR image by a detection-guided mixed-supervised segmentation network. Med Image Anal. 2022;75:102261.

PubMed  Google Scholar 

Gaonkar B, Beckett J, Attiah M, Ahn C, Edwards M, Wilson B, Laiwalla A, Salehi B, Yoo B, Bui AAT et al. Eigenrank by committee: Von Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation. Med Image Anal 2021, 67.

Iriondo C, Pedoia V, Majumdar S. Lumbar intervertebral disc characterization through quantitative MRI analysis: an automatic voxel-based relaxometry approach. Magn Reson Med. 2020;84(3):1376–90.

PubMed  PubMed Central  Google Scholar 

Qinhong D, Yue H, Wendong B, Yukun D, Huan Y, Yongming X. MAS-Net:multi-modal Assistant Segmentation Network for lumbar intervertebral disc. Phys Med Biol 2023, 68(17).

Gong H, Liu J, Chen B, Li S. ResAttenGAN: simultaneous segmentation of multiple spinal structures on axial lumbar MRI image using residual attention and adversarial learning. Artif Intell Med 2022, 124.

Rehman F, Shah SIA, Riaz N, Gilani SO. A Robust Scheme of Vertebrae Segmentation for Medical diagnosis. Ieee Access. 2019;7:120387–98.

Google Scholar 

Kuang X, Cheung JPY, Wong KK, Lam WY, Lam CH, Choy RW, Cheng CP, Wu H, Yang C, Wang K, et al. Spine-GFlow: a hybrid learning framework for robust multi-tissue segmentation in lumbar MRI without manual annotation. Comput Med Imaging Graph. 2022;99:102091.

PubMed  Google Scholar 

Pang S, Pang C, Zhao L, Chen Y, Su Z, Zhou Y, Huang M, Yang W, Lu H, Feng Q. SpineParseNet: spine parsing for volumetric MR image by a two-stage Segmentation Framework with semantic image representation. IEEE Trans Med Imaging. 2021;40(1):262–73.

PubMed  Google Scholar 

Han Z, Wei B, Xi X, Chen B, Yin Y, Li S. Unifying neural learning and symbolic reasoning for spinal medical report generation. Med Image Anal 2021, 67.

Wang H, Chen Y, Jiang T, Bian H, Shen X. 3D multi-scale feature extraction and recalibration network for spinal structure and lesion segmentation. Acta Radiol (Stockholm Sweden: 1987). 2023;64(12):3015–23.

Google Scholar 

Pang C, Su Z, Lin L, Lin G, He J, Lu H, Feng Q, Pang S. Automated measurement of spine indices on axial MR images for lumbar spinal stenosis diagnosis using segmentation-guided regression network. Med Phys. 2023;50(1):104–16.

PubMed  Google Scholar 

Coppock JA, Zimmer NE, Spritzer CE, Goode AP, DeFrate LE. Automated segmentation and prediction of intervertebral disc morphology and uniaxial deformations from MRI. Osteoarthr Cartil open. 2023;5(3):100378.

PubMed  PubMed Central  Google Scholar 

He S, Li Q, Li X, Zhang M. Automatic aid diagnosis report generation for lumbar disc MR image based on lightweight artificial neural networks. Biomed Signal Process Control 2023, 86.

Cheng YK, Lin CL, Huang YC, Chen JC, Lan TP, Lian ZY, Chuang CH. Automatic Segmentation of Specific Intervertebral Discs through a two-stage MultiResUNet Model. J Clin Med 2021, 10(20).

Saenz-Gamboa JJ, Domenech J, Alonso-Manjarres A, Gomez JA, Iglesia-Vaya, Mdl. Automatic semantic segmentation of the lumbar spine: clinical applicability in a multi-parametric and multi-center study on magnetic resonance images. Artif Intell Med 2023, 140.

Soydan Z, Bayramoglu E, Karasu R, Sayin I, Salturk S, Uvet H. An Automatized Deep Segmentation and classification model for lumbar disk degeneration and clarification of its impact on clinical decisions. Global Spine J 2023:21925682231200783.

Al-Kafri AS, Sudirman S, Hussain A, Al-Jumeily D, Natalia F, Meidia H, Afriliana N, Al-Rashdan W, Bashtawi M, Al-Jumaily M. Boundary delineation of MRI images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks. IEEE Access. 2019;7:43487–501.

Google Scholar 

Sustersic T, Rankovic V, Milovanovic V, Kovacevic V, Rasulic L, Filipovic N. A deep learning model for automatic detection and classification of disc herniation in magnetic resonance images. Ieee J Biomedical Health Inf. 2022;26(12):6036–46.

Google Scholar 

Suri A, Jones BC, Ng G, Anabaraonye N, Beyrer P, Domi A, Choi G, Tang S, Terry A, Leichner T et al. A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs. Bone 2021, 149.

Wang M, Su Z, Liu Z, Chen T, Cui Z, Li S, Pang S, Lu H. Deep learning-based automated magnetic resonance image segmentation of the lumbar structure and its adjacent structures at the L4/5 level. Bioeng (Basel Switzerland) 2023, 10(8).

Zheng H-D, Sun Y-L, Kong D-W, Yin M-C, Chen J, Lin Y-P, Ma X-F, Wang H-S, Yuan G-J, Yao M et al. Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI. Nat Commun 2022, 13(1).

Deng Y, Gu F, Zeng D, Lu J, Liu H, Hou Y, Zhang Q. An effective U-Net and BiSeNet complementary network for spine segmentation. Biomed Signal Process Control 2024, 89.

Kim S, Bae WC, Masuda K, Chung CB, Hwang D. Fine-grain segmentation of the intervertebral discs from MR Spine images using deep convolutional neural networks: BSU-Net. Appl Sci (Basel Switzerland) 2018, 8(9).

Mbarki W, Bouchouicha M, Tshienda FT, Moreau E, Sayadi M. Herniated lumbar disc generation and classification using cycle generative adversarial networks on Axial View MRI. Electronics 2021, 10(8).

He S, Li Q, Li X, Zhang M. A lightweight convolutional neural network based on dynamic level-set loss function for spine MR Image Segmentation. J Magn Reson Imaging. 2024;59(4):1438–53.

PubMed  Google Scholar 

Altun S, Alkan A. LSS-net: 3‐dimensional segmentation of the spinal canal for the diagnosis of lumbar spinal stenosis. Int J Imaging Syst Technol. 2022;33(1):378–88.