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Emerging trends and research foci of deep learning in spine: bibliometric and visualization study

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Abstract

As the cognition of spine develops, deep learning (DL) emerges as a powerful tool with tremendous potential for advancing research in this field. To provide a comprehensive overview of DL-spine research, our study utilized bibliometric and visual methods to retrieve relevant articles from the Web of Science database. VOSviewer and CiteSpace were primarily used for literature measurement and knowledge graph analysis. A total of 273 studies focusing on deep learning in the spine, with a combined total of 2302 citations, were retrieved. Additionally, the overall number of articles published on this topic demonstrated a continuous upward trend. China was the country with the highest number of publications, whereas the USA had the most citations. The two most prominent journals were “European Spine Journal” and “Medical Image Analysis,” and the most involved research area was Radiology Nuclear Medicine Medical Imaging. VOSviewer identified three visually distinct clusters: “segmentation,” “area,” and “neural network.” Meanwhile, CiteSpace highlighted “magnetic resonance image” and “lumbar” as the keywords with the longest usage, and “agreement” and “automated detection” as the most commonly used keywords. Although the application of DL in spine is still in its infancy, its future is promising. Intercontinental cooperation, extensive application, and more interpretable algorithms will invigorate DL in the field of spine.

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References

  1. Rupp TK, Ehlers W, Karajan N, Günther M, Schmitt S (2015) A forward dynamics simulation of human lumbar spine flexion predicting the load sharing of intervertebral discs, ligaments, and muscles. Biomech Model Mechanobiol 14(5):1081–1105

    Article  CAS  PubMed  Google Scholar 

  2. Fehlings MG, Tetreault L, Nater A, Choma T, Harrop J, Mroz T, Santaguida C, Smith JS (2015) The aging of the global population: the changing epidemiology of disease and spinal disorders. Neurosurgery 77(Suppl 4):S1–S5

    Article  PubMed  Google Scholar 

  3. Oei MW, Evens AL, Bhatt AA, Garner HW (2022) Imaging of the aging spine. Radiol Clin North Am 60(4):629–640

    Article  PubMed  Google Scholar 

  4. Lacout A, Lebreton C, Mompoint D, Mokhtari S, Vallée CA, Carlier RY (2009) CT and MRI of spinal neuroarthropathy. AJR Am J Roentgenol 193(6):W505–W514

    Article  PubMed  Google Scholar 

  5. Kochanski RB, Lombardi JM, Laratta JL, Lehman RA, O'Toole JE (2019) Image-guided navigation and robotics in spine surgery. Neurosurgery 84(6):1179–1189

    Article  PubMed  Google Scholar 

  6. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  PubMed  Google Scholar 

  7. Deo RC (2015) Machine learning in medicine. Circulation 132(20):1920–1930

    Article  PubMed  PubMed Central  Google Scholar 

  8. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP (2020) Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol 9(2):14

    PubMed  PubMed Central  Google Scholar 

  9. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25(1):24–29

    Article  CAS  PubMed  Google Scholar 

  10. Chan HP, Samala RK, Hadjiiski LM, Zhou C (2020) Deep learning in medical image analysis. Adv Exp Med Biol 1213:3–21

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A (2019) A primer on deep learning in genomics. Nat Genet 51(1):12–18

    Article  CAS  PubMed  Google Scholar 

  12. Duan H, Wang P, Huang Y, Xu G, Wei W, Shen X (2021) Robotics dexterous grasping: the methods based on point cloud and deep learning. Front Neurorobot 15:658280

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hallinan J, Zhu L, Yang K, Makmur A, Algazwi DAR, Thian YL, Lau S, Choo YS, Eide SE, Yap QV et al (2021) Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI. Radiology 300(1):130–138

    Article  PubMed  Google Scholar 

  14. Yeh YC, Weng CH, Huang YJ, Fu CJ, Tsai TT, Yeh CY (2021) Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs. Sci Rep 11(1):7618

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Goedmakers CMW, Lak AM, Duey AH, Senko AW, Arnaout O, Groff MW, Smith TR, Vleggeert-Lankamp CLA, Zaidi HA, Rana A et al (2021) Deep learning for adjacent segment disease at preoperative MRI for cervical radiculopathy. Radiology 301(3):664–671

    Article  PubMed  Google Scholar 

  16. Qu B, Cao J, Qian C, Wu J, Lin J, Wang L, Ou-Yang L, Chen Y, Yan L, Hong Q et al (2022) Current development and prospects of deep learning in spine image analysis: a literature review. Quant Imaging Med Surg 12(6):3454–3479

    Article  PubMed  PubMed Central  Google Scholar 

  17. Liu C, Yu R, Zhang J, Wei S, Xue F, Guo Y, He P, Shang L, Dong W (2022) Research hotspot and trend analysis in the diagnosis of inflammatory bowel disease: a machine learning bibliometric analysis from 2012 to 2021. Front Immunol 13:972079

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhao J, Lu Y, Qian Y, Luo Y, Yang W (2022) Emerging trends and research foci in artificial intelligence for retinal diseases: bibliometric and visualization study. J Med Internet Res 24(6):e37532

    Article  PubMed  PubMed Central  Google Scholar 

  19. Xia D, Chen G, Wu K, Yu M, Zhang Z, Lu Y, Xu L, Wang Y (2022) Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011-2021: a bibliometric analysis. Front Public Health 10:990708

    Article  PubMed  PubMed Central  Google Scholar 

  20. Stout NL, Alfano CM, Belter CW, Nitkin R, Cernich A, Lohmann Siegel K, Chan L (2018) A bibliometric analysis of the landscape of cancer rehabilitation research (1992-2016). J Natl Cancer Inst 110(8):815–824

    Article  PubMed  PubMed Central  Google Scholar 

  21. Waqas A, Salminen J, Jung SG, Almerekhi H, Jansen BJ (2019) Mapping online hate: a scientometric analysis on research trends and hotspots in research on online hate. PLoS One 14(9):e0222194

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hirsch JE (2007) Does the H index have predictive power? Proc Natl Acad Sci U S A 104(49):19193–19198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Roldan-Valadez E, Salazar-Ruiz SY, Ibarra-Contreras R, Rios C (2019) Current concepts on bibliometrics: a brief review about impact factor, Eigenfactor score, CiteScore, SCImago Journal Rank, Source-Normalised Impact per Paper, H-index, and alternative metrics. Ir J Med Sci 188(3):939–951

    Article  PubMed  Google Scholar 

  24. Yu Y, Li Y, Zhang Z, Gu Z, Zhong H, Zha Q, Yang L, Zhu C, Chen E (2020) A bibliometric analysis using VOSviewer of publications on COVID-19. Ann Transl Med 8(13):816

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Luo H, Cai Z, Huang Y, Song J, Ma Q, Yang X, Song Y (2021) Study on pain catastrophizing from 2010 to 2020: a bibliometric analysis via CiteSpace. Front Psychol 12:759347

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  PubMed  Google Scholar 

  27. Deng H, Qiao H, Dai Q, Ma C (2021) Deep learning in photoacoustic imaging: a review. J Biomed Opt 26(4)

  28. Rezaeilouyeh H, Mollahosseini A, Mahoor MH (2016) Microscopic medical image classification framework via deep learning and shearlet transform. J Med Imaging (Bellingham) 3(4):044501

    Article  PubMed  Google Scholar 

  29. Kokabu T, Kanai S, Kawakami N, Uno K, Kotani T, Suzuki T, Tachi H, Abe Y, Iwasaki N, Sudo H (2021) An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection. Spine J 21(6):980–987

    Article  PubMed  Google Scholar 

  30. Tsai JY, Hung IY, Guo YL, Jan YK, Lin CY, Shih TT, Chen BB, Lung CW (2021) Lumbar disc herniation automatic detection in magnetic resonance imaging based on deep learning. Front Bioeng Biotechnol 9:708137

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lemay A, Gros C, Zhuo Z, Zhang J, Duan Y, Cohen-Adad J, Liu Y (2021) Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning. Neuroimage Clin 31:102766

    Article  PubMed  PubMed Central  Google Scholar 

  32. Saravi B, Zink A, Ülkümen S, Couillard-Despres S, Hassel F, Lang G (2022) Performance of artificial intelligence-based algorithms to predict prolonged length of stay after lumbar decompression surgery. J Clin Med 11(14)

  33. Yagi M, Hosogane N, Fujita N, Okada E, Tsuji O, Nagoshi N, Asazuma T, Tsuji T, Nakamura M, Matsumoto M et al (2019) Predictive model for major complications 2 years after corrective spine surgery for adult spinal deformity. Eur Spine J 28(1):180–187

    Article  PubMed  Google Scholar 

  34. de Jonge MC, Kramer J (2014) Spine and sport. Semin Musculoskelet Radiol 18(3):246–264

    Article  PubMed  Google Scholar 

  35. Lessmann N, van Ginneken B, de Jong PA, Išgum I (2019) Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med Image Anal 53:142–155

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  37. Horng MH, Kuok CP, Fu MJ, Lin CJ, Sun YN (2019) Cobb angle measurement of spine from X-ray images using convolutional neural network. Comput Math Methods Med 2019:6357171

    Article  PubMed  PubMed Central  Google Scholar 

  38. Al Arif S, Knapp K, Slabaugh G (2018) Fully automatic cervical vertebrae segmentation framework for X-ray images. Comput Methods Programs Biomed 157:95–111

    Article  PubMed  Google Scholar 

  39. Galbusera F, Niemeyer F, Wilke HJ, Bassani T, Casaroli G, Anania C, Costa F, Brayda-Bruno M, Sconfienza LM (2019) Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach. Eur Spine J 28(5):951–960

    Article  PubMed  Google Scholar 

  40. Orbach J (1962) Principles of neurodynamics. perceptrons and the theory of brain mechanisms. Archives of General Psychiatry 7(3):218–219

    Article  Google Scholar 

  41. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42(11):226

    Article  PubMed  Google Scholar 

  42. Wang L, Du J, Gholipour A, Zhu H, He Z, Jia Y (2021) 3D dense convolutional neural network for fast and accurate single MR image super-resolution. Comput Med Imaging Graph 93:101973

    Article  PubMed  Google Scholar 

  43. Liu D, Jia Z, Jin M, Liu Q, Liao Z, Zhong J, Ye H, Chen G (2020) Cardiac magnetic resonance image segmentation based on convolutional neural network. Comput Methods Programs Biomed 197:105755

    Article  PubMed  Google Scholar 

  44. Zhou Y, Liu Y, Chen Q, Gu G, Sui X (2019) Automatic lumbar MRI detection and identification based on deep learning. J Digit Imaging 32(3):513–520

    Article  PubMed  Google Scholar 

  45. Lim DSW, Makmur A, Zhu L, Zhang W, Cheng AJL, Sia DSY, Eide SE, Ong HY, Jagmohan P, Tan WC et al (2022) Improved productivity using deep learning-assisted reporting for lumbar spine MRI. Radiology 305(1):160–166

    Article  PubMed  Google Scholar 

  46. Natalia F, Young JC, Afriliana N, Meidia H, Yunus RE, Sudirman S (2022) Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier. PLoS One 17(1):e0261659

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Gao KT, Tibrewala R, Hess M, Bharadwaj UU, Inamdar G, Link TM, Chin CT, Pedoia V, Majumdar S (2022) Automatic detection and voxel-wise mapping of lumbar spine Modic changes with deep learning. JOR Spine 5(2):e1204

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hsieh CI, Zheng K, Lin C, Mei L, Lu L, Li W, Chen FP, Wang Y, Zhou X, Wang F et al (2021) Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning. Nat Commun 12(1):5472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Suri A, Jones BC, Ng G, Anabaraonye N, Beyrer P, Domi A, Choi G, Tang S, Terry A, Leichner T et al (2021) A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs. Bone 149:115972

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ha J, Park T, Kim HK, Shin Y, Ko Y, Kim DW, Sung YS, Lee J, Ham SJ, Khang S et al (2021) Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography. Sci Rep 11(1):21656

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This research was funded by the National Natural Science Foundation of China (81701199) of Ming Li.

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Author notes

  1. Kai Chen, Xiao Zhai, and Sheng Wang contributed equally to this article.

Authors and Affiliations

  1. Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China

    Kai Chen, Xiao Zhai, Xiaoyu Li & Ming Li

  2. Department of Emergency, Shanghai Changhai Hospital, Shanghai, China

    Sheng Wang

  3. Department of Orthopedics, No. 906 Hospital of Joint Logistic Support Force of PLA, Ningbo, Zhejiang, China

    Zhikai Lu

  4. Luodian Clinical Drug Research Center, Shanghai Baoshan Luodian Hospital, Shanghai University, Shanghai, China

    Demeng Xia

  5. Emergency Department, Naval Hospital of Eastern Theater, Zhoushan, Zhejiang, China

    Demeng Xia

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Contributions

Conceptualization, DM.X. and K.C.; methodology, XY.L; software, S.W.; validation, ZK.L. and X.Z.; formal analysis, M.L.; investigation, M.L.; resources, X.Z.; data curation, DM.X.; writing—original draft preparation, K.C.; writing—review and editing, DM.X.; visualization, X.Z.; supervision, ZK.L.; project administration, X.Z.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zhikai Lu, Demeng Xia or Ming Li.

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Chen, K., Zhai, X., Wang, S. et al. Emerging trends and research foci of deep learning in spine: bibliometric and visualization study. Neurosurg Rev 46, 81 (2023). https://doi.org/10.1007/s10143-023-01987-5

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