ldtЛучевая диагностика и терапияDiagnostic radiology and radiotherapy2079-5343Baltic Medical Education Center10.22328/2079-5343-2026-17-2-31-42ldt-1246Research ArticleЛЕКЦИИ И ОБЗОРЫLECTURES AND REVIEWSВозможности искусственного интеллекта в классификации патологий позвоночника на современном этапе развития: систематический обзорPossibilities of artificial intelligence in classification of spinal pathologies at the present stage of development: a systematic reviewhttps://orcid.org/0009-0006-2726-1392ВасильевК. О.VasilyevK. O.

Васильев Константин Олегович - врач-рентгенолог отделения лучевой диагностики ; ассистент кафедры лучевой диагностики стоматологического факультета   

630091, Новосибирск, ул. Фрунзе, д. 17; 630091, Новосибирск, ул. Красный проспект, д. 52

Konstantin O. Vasiliev - radiologist of the department of radiation diagnostics ; Assistant at the Department of Radiation Diagnostics of the Dental Faculty

630091, Novosibirsk, st. Frunze, 17; 630091, Novosibirsk, st. Krasny Prospekt, 52

vasiliev_ko@mail.ruhttps://orcid.org/0000-0002-3411-508XЛукиновВ. Л.LukinovV. L.

Лукинов Виталий Леонидович - кандидат физико-математических наук, ведущий научный сотрудник научно-исследовательского отдела проектной и инновационной деятельности

630091, Новосибирск, ул. Фрунзе, д. 17

Vitaly L. Lukinov - Cand. of Sci. (Phys. and Math.), leading researcher of the research department of project and innovation activities;  Professor of the Department of Traumatology and Orthopedics 

630091, Novosibirsk, st. Frunze, 17

vitaliy.lukinov@ssccl.ruhttps://orcid.org/0000-0001-8545-0024РерихВ. В.RerikhV. V.

Рерих Виктор Викторович - доктор медицинских наук, врач травматолог-ортопед, начальник научно-исследовательского отделения патологии позвоночника ; профессор кафедры травматологии и ортопедии

630091, Новосибирск, ул. Фрунзе, д. 17;  630091, Новосибирск, ул. Красный проспект, д. 52

Viktor V. Rerikh - Dr. of Sci. (Med.), traumatologist-orthopedist, head of the research department of spine pathology ; Professor of the Department of Traumatology and Orthopedics

630091, Novosibirsk, st. Frunze, 17

rvv_nsk@mail.ruНовосибирский научно-исследовательский институт травматологии и ортопедии имени Я.Л.Цивьяна; Новосибирский государственный медицинский университетРоссияNovosibirsk Research Institute of Traumatology and Orthopedics named after Ya. L. Tsivyan; Novosibirsk State Medical UniversityRussian FederationНовосибирский научно-исследовательский институт травматологии и ортопедии имени Я.Л.ЦивьянаРоссияNovosibirsk Research Institute of Traumatology and Orthopedics named after Ya. L. TsivyanRussian Federation2026240720261723142Copyright © Васильев К.О., Лукинов В.Л., Рерих В.В., 20262026Васильев К.О., Лукинов В.Л., Рерих В.В.Vasilyev K.O., Lukinov V.L., Rerikh V.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://radiag.bmoc-spb.ru/jour/article/view/1246ВВЕДЕНИЕ

ВВЕДЕНИЕ: Большой объем информации, в том числе растущая численность населения и количество исследований значительно повышает нагрузку на врача любой специальности, в частности на врача-рентгенолога. Цифровизация исследований позволяет облегчить процессы диагностики, ведь грамотное программное обеспечение помогает специалисту быстрее обнаружить патологический процесс, а значит быстрее начать столь необходимое лечение. Одной из наиболее быстро развивающихся и важных моделей программного обеспечения для этих целей являются нейросети.

ЦЕЛЬ

ЦЕЛЬ: Определение возможностей нейросетей в вертебрологии на современном этапе их развития в области такой функции нейросетей, как классификация.

МАТЕРИАЛЫ И МЕТОДЫ

МАТЕРИАЛЫ И МЕТОДЫ: При помощи протокола PRISMA был произведен поиск в базе данных Pubmed за период с января 2017 по 31 декабря 2023 г. при помощи ключевых слов.

РЕЗУЛЬТАТЫ

РЕЗУЛЬТАТЫ: Для систематического обзора было отобрано 34 статьи, в которых описывалась такая функция нейросетей, как классификация.

ОБСУЖДЕНИЕ

ОБСУЖДЕНИЕ: По результатам анализа источников литературы сделаны выводы о полезности применения искусственного интеллекта на современном этапе развития в вертебрологии в такой функции, как классификация.

ЗАКЛЮЧЕНИЕ

ЗАКЛЮЧЕНИЕ: Для такой функции нейросети, как классификация, в отношении патологических изменений позвоночника не все так однозначно. В выявлении дегенеративных изменений хорошие результаты касаются в основном только центрального стеноза позвоночного канала, а результаты выявления стенозов боковых карманов и фораминальных стенозов неопределенные. Что касается переломов позвоночника, то здесь наметился абсолютный лидер — GoogleNet. За GoogleNet лидирует две сети — DCNN собственной разработки (Germann C. et al.) и комбинации многослойного перцептрона и методик 3D-радиомики (Chiari-Correia N.S. et al.). В задачи классификации опухолей позвоночника выделяется только одна сеть — ResNet 50, результаты же прочих архитектур значительно отстают и нуждаются в дальнейшей доработке.

INTRODUCTION

INTRODUCTION: A large amount of information, including a growing population and the number of studies significantly increases the workload of a doctor of any specialty, including a radiologist. Digitalization of research makes it possible to facilitate diagnostic processes, because competent software helps a specialist to quickly detect a pathological process, and therefore — to start muchneeded treatment faster. One of the most developing and important software models for these purposes is neural networks. 

OBJECTIVE

OBJECTIVE: To determine the capabilities of neural networks in vertebrology at the current stage of their development in the field of such a neural network function as classification.

MATERIALS AND METHODS

MATERIALS AND METHODS: Using the PRISMA protocol, a search was performed in the Pubmed database for the period from January 2017 to December 31, 2023 using keywords. 

RESULTS

RESULTS: For the systematic review, 34 articles were selected that described such a neural network function as classification. 

DISCUSSION

DISCUSSION: Based on the analysis of literature sources, conclusions were drawn about the usefulness of using artificial intelligence at the current stage of development in vertebrology in such a function as classification. 

CONCLUSION

CONCLUSION: The classification function of neural networks for pathological changes in the spine is not so clear-cut. Good results in detecting degenerative changes are primarily limited to central spinal stenosis, while the results for detecting lateral recess and foraminal stenosis are inconclusive. For spinal fractures, GoogleNet has emerged as the clear leader. Two networks follow GoogleNet: a proprietary DCNN (Germann C. et al.) and a combination of a multilayer perceptron and 3D radiomics techniques (Chiari-Correia N. S. et al.). Only one network, ResNet 50, excels in the classification of spinal tumors, while the results of other architectures lag significantly behind and require further refinement.

искусственный интеллектнейросетивертебрологияпозвоночникклассификацияartificial intelligenceneural networksvertebrologyspineclassificationReferencesJamshidi M., Rajabian M., Avery M.B. et al. A novel self-expanding primarily bioabsorbable braided flow-diverting stent for aneurysms: initial safety results // Journal of neurointerventional surgery. 2020. Vol. 12, No. 7. P. 700–705. doi: 10.1136/neurintsurg-2019-015555.Jamshidi M., Rajabian M., Avery M.B. et al. A novel self-expanding primarily bioabsorbable braided flow-diverting stent for aneurysms: initial safety results // Journal of neurointerventional surgery . 2020. Vol. 12, No. 7. P. 700–705. doi: 10.1136/neurintsurg-2019-015555.Swart N., Maroni R., Muldrew B. et al. Economic evaluation of Cytosponge®-trefoil factor 3 for Barrett esophagus: A cost-utility analysis of randomised controlled trial data // EClinicalMedicine. 2021. Vol. 37. doi: 10.1016/j.eclinm.2021.100969.Swart N., Maroni R., Muldrew B. et al. Economic evaluation of Cytosponge®-trefoil factor 3 for Barrett esophagus: A cost-utility analysis of randomised controlled trial data // EClinicalMedicine . 2021. Vol. 37. doi: 10.1016/j.eclinm.2021.100969.Albers G.W., Marks M.P., Kemp S. et al. Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging: Albers G.W., Marks M.P., Kemp S. et al. // N. Engl. J. Med. 2018. Vol. 378. Р. 708–718 // Journal of Emergency Medicine. 2018. Vol. 55, No. 1. P. 151. doi: 10.1056/NEJMoa1713973Albers G.W., Marks M.P., Kemp S. et al. Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging: Albers G.W., Marks M.P., Kemp S. et al. // N. Engl. J. Med . 2018. Vol. 378. Р. 708–718 // Journal of Emergency Medicine . 2018. Vol. 55, No. 1. P. 151. doi: 10.1056/NEJMoa1713973Thong L.T., Chou H.S., Chew H.S.J., Lau Y. Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and metaanalysis // Lung Cancer. 2023. Vol. 176. P. 4–13. doi: 10.1016/j.lungcan.2022.12.002.Thong L.T., Chou H.S., Chew H.S.J., Lau Y. Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and metaanalysis // Lung Cancer . 2023. Vol. 176. P. 4–13. doi: 10.1016/j.lungcan.2022.12.002.Gautam N., Saluja P., Malkawi A. et al. Current and future applications of artificial intelligence in coronary artery disease // Healthcare. MDPI. 2022. Vol. 10, No. 2. P. 232. doi: 10.3390/healthcare10020232.Gautam N., Saluja P., Malkawi A. et al. Current and future applications of artificial intelligence in coronary artery disease // Healthcare. MDPI . 2022. Vol. 10, No. 2. P. 232. doi: 10.3390/healthcare10020232.Roberts M., Driggs D., Thorpe M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans // Nature Machine Intelligence. 2021. Vol. 3, No. 3. P. 199–217. doi: 10.1038/s42256-021-00307-0.Roberts M., Driggs D., Thorpe M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans // Nature Machine Intelligence . 2021. Vol. 3, No. 3. P. 199–217. doi: 10.1038/s42256-021-00307-0.Wang Z., Liu X., Hu X. et al. Age-related changes in cervical sagittal alignment: based on 625 Chinese asymptomatic subjects // European Spine Journal. 2023. Vol. 32, No. 5. P. 1607–1615. doi: 10.1007/s00586-023-07632-5.Wang Z., Liu X., Hu X. et al. Age-related changes in cervical sagittal alignment: based on 625 Chinese asymptomatic subjects // European Spine Journal . 2023. Vol. 32, No. 5. P. 1607–1615. doi: 10.1007/s00586-023-07632-5.Tricco A.C., Lillie E., Zarin W., O’Brien K.K., Colquhoun H., Levac. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation // Annals of internal medicine. 2018. Vol. 169, No. 7. P. 467–473. doi: 10.7326/M18-0850.Tricco A.C., Lillie E., Zarin W., O’Brien K.K., Colquhoun H., Levac. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation // Annals of internal medicine . 2018. Vol. 169, No. 7. P. 467–473. doi: 10.7326/M18-0850.Liawrungrueang W., Kim P., Kotheeranurak V., Jitpakdee K., Sarasombath Automatic detection, classification, and grading of lumbar intervertebral disc degeneration using an artificial neural network model // Diagnostics. 2023. Vol. 13, No. 4. P. 663. doi: 10.3390/diagnostics13040663.Liawrungrueang W., Kim P., Kotheeranurak V., Jitpakdee K., Sarasombath Automatic detection, classification, and grading of lumbar intervertebral disc degeneration using an artificial neural network model // Diagnostics . 2023. Vol. 13, No. 4. P. 663. doi: 10.3390/diagnostics13040663.Mastouri R., Khlifa N., Neji H., Hantous-Zannad S. A bilinear convolutional neural network for lung nodules classification on CT images // International Journal of Computer Assisted Radiology and Surgery. 2021. Vol. 16. P. 91–101. doi: 10.1007/s11548-020-02283-z.Mastouri R., Khlifa N., Neji H., Hantous-Zannad S. A bilinear convolutional neural network for lung nodules classification on CT images // International Journal of Computer Assisted Radiology and Surgery . 2021. Vol. 16. P. 91–101. doi: 10.1007/s11548-020-02283-z.Merali Z., Wang J.Z., Badhiwala J.H. et al. A deep learning model for detection of cervical spinal cord compression in MRI scans // Scientific reports. 2021. Vol. 11, No. 1. P. 1–11. doi: 10.1038/s41598-021-89848-3.Merali Z., Wang J.Z., Badhiwala J.H. et al. A deep learning model for detection of cervical spinal cord compression in MRI scans // Scientific reports . 2021. Vol. 11, No. 1. P. 1–11. doi: 10.1038/s41598-021-89848-3.Kumar K.K., Gopal T.V. A novel approach to self order feature reweighting in CBIR to reduce semantic gap using Relevance Feedback // 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014]. 2014. P. 1437–1442. doi: 10.1109/ICCPCT.2014.7054919.Kumar K.K., Gopal T.V. A novel approach to self order feature reweighting in CBIR to reduce semantic gap using Relevance Feedback // 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014] . 2014. P. 1437–1442. doi: 10.1109/ICCPCT.2014.7054919.Абдуллина К.М., Спивак А.И. Применение нейронной сети архитектуры U-Net для задачи локализации нарушений целостности цифровых изображений // Научно-технический вестник информационных технологий, механики и оптики. 2020. Т. 20, № 3. С. 425–431.Abdullina K.M., Spivak A.I. Application of the U-Net neural network architecture for the problem of localizing integrity violations of digital images. Scientific and Technical Bulletin of Information Technologies, Mechanics and Optics , 2020, Vol. 20, No. 3, рр. 425–431 (In Russ.). doi: 10.17586/2226-1494-2020-20-3-425-431.Hallinan J.T.P.D., Zhu L., Yang K… et al. Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI // Radiology. 2021. Vol. 300, No. 1. P. 130–138. doi: 10.1148/radiol.2021204289.Hallinan J.T.P.D., Zhu L., Yang K… et al. Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI // Radiology . 2021. Vol. 300, No. 1. P. 130–138. doi: 10.1148/radiol.2021204289.Lehnen N.C., Haase R., Faber J. et al. Detection of degenerative changes on MR images of the lumbar spine with a convolutional neural network: a feasibility study // Diagnostics. 2021. Vol. 11, No. 5. P. 902. doi: 10.3390/diagnostics11050902.Lehnen N.C., Haase R., Faber J. et al. Detection of degenerative changes on MR images of the lumbar spine with a convolutional neural network: a feasibility study // Diagnostics . 2021. Vol. 11, No. 5. P. 902. doi: 10.3390/diagnostics11050902.Jamaludin A., Lootus M., Kadir T. et al. ISSLS prize in bioengeneering science 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist // European spine journal. 2017. Vol. 26, No. 5. P. 1374–1383. doi: 10.1007/s00586-017-4956-3.Jamaludin A., Lootus M., Kadir T. et al. ISSLS prize in bioengeneering science 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist // European spine journal . 2017. Vol. 26, No. 5. P. 1374–1383. doi: 10.1007/s00586-017-4956-3.Lu J. T., Pedemonte S., Bizzo B. et al. Deep Spine: Automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning // Machine Learning for Healthcare Conference // PMLR. 2018. P. 403–419. doi: 10.48550/arXiv.1807.10215.Lu J. T., Pedemonte S., Bizzo B. et al. Deep Spine: Automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning // Machine Learning for Healthcare Conference // PMLR . 2018. P. 403–419. doi: 10.48550/arXiv.1807.10215.Won D., Lee H.J., Lee S.J., Park S.H. Spinal stenosis grading in magnetic resonance imaging using deep convolutional neural networks // Spine. 2020. Vol. 45, No. 12. P. 804–812. doi: 10.1097/BRS.0000000000003377.Won D., Lee H.J., Lee S.J., Park S.H. Spinal stenosis grading in magnetic resonance imaging using deep convolutional neural networks // Spine . 2020. Vol. 45, No. 12. P. 804–812. doi: 10.1097/BRS.0000000000003377.Han Z., Wei B., Leung S., Chung J., Li S. Towards automatic report generation in spine radiology using weakly supervised framework // Medical Image Computing and Computer Assisted Intervention–MICCAI. 2018: 21[st] International Conference, Granada, Spain, September 16–20, 2018, Proceedings, Part IV 11. Springer International Publishing, 2018. P. 185–193. doi: 10.1007/978-3-030-00937-3_22.Han Z., Wei B., Leung S., Chung J., Li S. Towards automatic report generation in spine radiology using weakly supervised framework // Medical Image Computing and Computer Assisted Intervention–MICCAI . 2018: 21[st] International Conference, Granada, Spain, September 16–20, 2018, Proceedings, Part IV 11. Springer International Publishing, 2018. P. 185–193. doi: 10.1007/978-3-030-00937-3_22.Merali Z., Wang J.Z., Badhiwala J.H. et al. A deep learning model for detection of cervical spinal cord compression in MRI scans // Scientific reports. 2021. Vol. 11, No. 1. P. 1–11. doi: 10.1038/s41598-021-89848-3.Merali Z., Wang J.Z., Badhiwala J.H. et al. A deep learning model for detection of cervical spinal cord compression in MRI scans // Scientific reports . 2021. Vol. 11, No. 1. P. 1–11. doi: 10.1038/s41598-021-89848-3.Lewandrowski K.U., Muraleedharan N., Eddy S.A. et al. Feasibility of deep learning algorithms for reporting in routine spine magnetic resonance imaging // International Journal of Spine Surgery. 2020. Vol. 14, No. s3. P. S86-S97. doi: 10.14444/7131.Lewandrowski K.U., Muraleedharan N., Eddy S.A. et al. Feasibility of deep learning algorithms for reporting in routine spine magnetic resonance imaging // International Journal of Spine Surgery. 2020. Vol. 14, No. s3. P. S86-S97. doi: 10.14444/7131.Ghauri M.S., Reddy A.J., Tak N. et al. Utilizing deep learning for X-ray imaging: detecting and classifying degenerative spinal conditions // Cureus. 2023. Vol. 15, No. 7. doi: 10.7759/cureus.41582.Ghauri M.S., Reddy A.J., Tak N. et al. Utilizing deep learning for X-ray imaging: detecting and classifying degenerative spinal conditions // Cureus . 2023. Vol. 15, No. 7. doi: 10.7759/cureus.41582.Gao K. T., Tibrewala R., Hess M., Bharadwaj U.U., Inamdar G. et al. Automatic detection and voxel‐wise mapping of lumbar spine Modic changes with deep learning // JOR spine. 2022. Vol. 5, No. 2. P. e1204. doi: 10.1002/jsp2.1204.Gao K. T., Tibrewala R., Hess M., Bharadwaj U.U., Inamdar G. et al. Automatic detection and voxel‐wise mapping of lumbar spine Modic changes with deep learning // JOR spine . 2022. Vol. 5, No. 2. P. e1204. doi: 10.1002/jsp2.1204.Zhang W., Chen Z., Su Z. et al. Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images // JOR spine. 2023. Vol. 6, No. 3. P. e1276. doi: 10.1002/jsp2.1276.Zhang W., Chen Z., Su Z. et al. Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images // JOR spine . 2023. Vol. 6, No. 3. P. e1276. doi: 10.1002/jsp2.1276.Bharadwaj U.U., Christine M., Li S. et al. Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI // European radiology. 2023. Vol. 33, No. 5. P. 3435–3443. doi: 10.1007/s00330-023-09483-6.Bharadwaj U.U., Christine M., Li S. et al. Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI // European radiology . 2023. Vol. 33, No. 5. P. 3435–3443. doi: 10.1007/s00330-023-09483-6.Liawrungrueang W., Kim P., Kotheeranurak V., Jitpakdee K., Sarasombath P. Automatic detection, classification, and grading of lumbar intervertebral disc degeneration using an artificial neural network model // Diagnostics. 2023. Vol. 13, No. 4. P. 663. doi: 10.3390/diagnostics13040663.Liawrungrueang W., Kim P., Kotheeranurak V., Jitpakdee K., Sarasombath P. Automatic detection, classification, and grading of lumbar intervertebral disc degeneration using an artificial neural network model // Diagnostics . 2023. Vol. 13, No. 4. P. 663. doi: 10.3390/diagnostics13040663.Dong Q. Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria // Academic radiology. 2022. Vol. 29, No. 12. P. 1819–1832. doi: 10.1016/j.acra.2022.02.020.Dong Q. Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria // Academic radiology . 2022. Vol. 29, No. 12. P. 1819–1832. doi: 10.1016/j.acra.2022.02.020.Germann C., Meyer A. N., Staib M., Sutter R., Fritz B. Performance of a deep convolutional neural network for MRI-based vertebral body measurements and insufficiency fracture detection // European Radiology. 2023. Vol. 33, No. 5. P. 3188–3199. doi: 10.1007/s00330-022-09354-6.Germann C., Meyer A. N., Staib M., Sutter R., Fritz B. Performance of a deep convolutional neural network for MRI-based vertebral body measurements and insufficiency fracture detection // European Radiology . 2023. Vol. 33, No. 5. P. 3188–3199. doi: 10.1007/s00330-022-09354-6.Chiari-Correia N.S., Nogueira-Barbosa M.H., Chiari-Correia R.D., Azevedo-Marques P.M. A 3D radiomics-based artificial neural network model for benign versus malignant vertebral compression fracture classification in MRI // Journal of Digital Imaging. 2023. Vol. 36, No. 4. P. 1565–1577. doi: 10.1007/s10278-023-00847-4.Chiari-Correia N.S., Nogueira-Barbosa M.H., Chiari-Correia R.D., Azevedo-Marques P.M. A 3D radiomics-based artificial neural network model for benign versus malignant vertebral compression fracture classification in MRI // Journal of Digital Imaging . 2023. Vol. 36, No. 4. P. 1565–1577. doi: 10.1007/s10278-023-00847-4.Doerr S. A., Weber-Levine C., Hersh A.M. et al. Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm // Neurosurgical focus. 2022. Vol. 52, No. 4. P. E5. doi: 10.3171/2022.1.FOCUS21745.Doerr S. A., Weber-Levine C., Hersh A.M. et al. Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm // Neurosurgical focus . 2022. Vol. 52, No. 4. P. E5. doi: 10.3171/2022.1.FOCUS21745.Bar A., Wolf L., Amitai O.B., Toledano E., Elnekave E. Compression fractures detection on CT // Medical imaging 2017: computer-aided diagnosis // SPIE. 2017. Vol. 10134. P. 1036–1043. doi: 10.1117/12.2249635.Bar A., Wolf L., Amitai O.B., Toledano E., Elnekave E. Compression fractures detection on CT // Medical imaging 2017: computer-aided diagnosis // SPIE . 2017. Vol. 10134. P. 1036–1043. doi: 10.1117/12.2249635.Small J.E., Osler P., Paul A.B., Kunst M. CT cervical spine fracture detection using a convolutional neural network // American Journal of Neuroradiology. 2021. Vol. 42, No. 7. P. 1341–1347. doi: 10.3174/ajnr.A7094.Small J.E., Osler P., Paul A.B., Kunst M. CT cervical spine fracture detection using a convolutional neural network // American Journal of Neuroradiology . 2021. Vol. 42, No. 7. P. 1341–1347. doi: 10.3174/ajnr.A7094.Tomita N., Cheung Y.Y., Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans // Computers in biology and medicine. 2018. Vol. 98. P. 8–15. doi: 10.1016/j.compbiomed.2018.05.011.Tomita N., Cheung Y.Y., Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans // Computers in biology and medicine . 2018. Vol. 98. P. 8–15. doi: 10.1016/j.compbiomed.2018.05.011.Derkatch S., Kirby C., Kimelman D. et al. Identification of vertebral fractures by convolutional neural networks to predict nonvertebral and hip fractures: a registrybased cohort study of dual X-ray absorptiometry // Radiology. 2019. Vol. 293, No. 2. P. 405–411. doi: 10.1148/radiol.2019190201.Derkatch S., Kirby C., Kimelman D. et al. Identification of vertebral fractures by convolutional neural networks to predict nonvertebral and hip fractures: a registrybased cohort study of dual X-ray absorptiometry // Radiology . 2019. Vol. 293, No. 2. P. 405–411. doi: 10.1148/radiol.2019190201.Zhang J., Liu F., Xu J. et al. Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography // Frontiers in endocrinology. 2023. Vol. 14. P. 1132725. doi: 10.3389/fendo.2023.1132725.Zhang J., Liu F., Xu J. et al. Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography // Frontiers in endocrinology . 2023. Vol. 14. P. 1132725. doi: 10.3389/fendo.2023.1132725.Dong Q., Luo G., Lane N.E. et al. Generalizability of deep learning classification of spinal osteoporotic compression fractures on radiographs using an adaptation of the modified-2 algorithm-based qualitative criteria // Academic radiology. 2023. Vol. 30, No. 12. P. 2973–2987. doi: 10.1016/j.acra.2023.04.023.Dong Q., Luo G., Lane N.E. et al. Generalizability of deep learning classification of spinal osteoporotic compression fractures on radiographs using an adaptation of the modified-2 algorithm-based qualitative criteria // Academic radiology . 2023. Vol. 30, No. 12. P. 2973–2987. doi: 10.1016/j.acra.2023.04.023.Boonrod A. et al. Diagnostic accuracy of deep learning for evaluation of C-spine injury from lateral neck radiographs // Ш. 2022. Vol. 8, No. 8. doi: 10.1016/j.heliyon.2022.e10372.Boonrod A. et al. Diagnostic accuracy of deep learning for evaluation of C-spine injury from lateral neck radiographs // Ш. 2022. Vol. 8, No. 8. doi: 10.1016/j.heliyon.2022.e10372.Chen X.G., Liu Y.A. classification method for thoracolumbar vertebral fractures due to basketball sports injury based on deep learning // Computational and mathematical methods in medicine. 2022. Vol. 2022, No. 1. P. 8747487. doi: 10.1155/2022/8747487.Chen X.G., Liu Y.A. classification method for thoracolumbar vertebral fractures due to basketball sports injury based on deep learning // Computational and mathematical methods in medicine . 2022. Vol. 2022, No. 1. P. 8747487. doi: 10.1155/2022/8747487.Yeh L.R., Zhang Y., Chen J.H. et al. A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet // European Spine Journal. 2022. P. 1–9. doi: 10.1007/s00586-022-07121-1.Yeh L.R., Zhang Y., Chen J.H. et al. A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet // European Spine Journal . 2022. P. 1–9. doi: 10.1007/s00586-022-07121-1.Liu B., Jin Y., Feng S. et al. Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist’s assessment // European Radiology. 2023. Vol. 33, No. 7. P. 5060–5068. doi: 10.1007/s00330-023-09713-x.Liu B., Jin Y., Feng S. et al. Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist’s assessment // European Radiology . 2023. Vol. 33, No. 7. P. 5060–5068. doi: 10.1007/s00330-023-09713-x.Xu F., Xiong Y., Ye G. et al. Deep learning-based artificial intelligence model for classification of vertebral compression fractures: A multicenter diagnostic study // Frontiers in Endocrinology. 2023. Vol. 14. P. 1025749. doi: 10.3389/fendo.2023.1025749.Xu F., Xiong Y., Ye G. et al. Deep learning-based artificial intelligence model for classification of vertebral compression fractures: A multicenter diagnostic study // Frontiers in Endocrinology . 2023. Vol. 14. P. 1025749. doi: 10.3389/fendo.2023.1025749.Lang N., Zhang Y., Zhang E. et al. Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI // Magnetic resonance imaging. 2019. Vol. 64. P. 4. doi: 10.1016/j.mri.2019.02.013.Lang N., Zhang Y., Zhang E. et al. Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI // Magnetic resonance imaging . 2019. Vol. 64. P. 4. doi: 10.1016/j.mri.2019.02.013.Chen K., Cao J., Zhang X. et al. Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network // Frontiers in Oncology. 2022. Vol. 12. P. 981769. doi: 10.3389/fonc.2022.981769.Chen K., Cao J., Zhang X. et al. Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network // Frontiers in Oncology . 2022. Vol. 12. P. 981769. doi: 10.3389/fonc.2022.981769.Hallinan J.T.P.D., Zhu L., Zhang W. et al. Deep learning model for classifying metastatic epidural spinal cord compression on MRI // Frontiers in Oncology. 2022. Vol. 12. P. 849447. doi: 10.3389/fonc.2022.849447.Hallinan J.T.P.D., Zhu L., Zhang W. et al. Deep learning model for classifying metastatic epidural spinal cord compression on MRI // Frontiers in Oncology . 2022. Vol. 12. P. 849447. doi: 10.3389/fonc.2022.849447.Xiong X., Wang J., Hu S. et al. Differentiating between multiple myeloma and metastasis subtypes of lumbar vertebra lesions using machine learning-based radiomics // Frontiers in Oncology. 2021. Vol. 11. P. 601699. doi: 10.3389/fonc.2021.601699.Xiong X., Wang J., Hu S. et al. Differentiating between multiple myeloma and metastasis subtypes of lumbar vertebra lesions using machine learning-based radiomics // Frontiers in Oncology . 2021. Vol. 11. P. 601699. doi: 10.3389/fonc.2021.601699.Hallinan J.T.P.D., Zhu L., Zhang W. et al. Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT // Frontiers in Oncology. 2023. Vol. 13. P. 1151073. doi: 10.3389/fonc.2023.1151073.Hallinan J.T.P.D., Zhu L., Zhang W. et al. Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT // Frontiers in Oncology . 2023. Vol. 13. P. 1151073. doi: 10.3389/fonc.2023.1151073.Ito S., Nakashima H., Segi N. et al. Automated detection and diagnosis of spinal schwannomas and meningiomas using deep learning and magnetic resonance imaging // J. Clin. Med. 2023. Vol. 12, No. 15. P. 5075. doi: 10.3390/jcm12155075.Ito S., Nakashima H., Segi N. et al. Automated detection and diagnosis of spinal schwannomas and meningiomas using deep learning and magnetic resonance imaging // J. Clin. Med . 2023. Vol. 12, No. 15. P. 5075. doi: 10.3390/jcm12155075.

The authors declare that there are no conflicts of interest present.