Opportunistic screening for osteoporosis using artificial intelligence services

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

BACKGROUND: An osteoporosis (OP) diagnosis technique based on routine CT examinations, which allows detecting radiological signs of OP, is currently being actively implemented. Given the issue of underdiagnosed compression fractures (CFs) on CT images, radiologists could benefit from artificial intelligence (AI) services.

AIM: This study aimed to assess the potential use of AI services for OP diagnosis based on routine CT findings for opportunistic screening.

METHODS: The project involved three health facilities (HFs). Chest CT scans obtained in these HFs between October 2022 and October 2023 in patients over 50 years of age were selected, in which AI services detected signs of OP (CFs and/or reduced vertebral bone density). All cases were re-evaluated by radiologists to identify potential errors made by the service. The final list of patients eligible for dual-energy X-ray absorptiometry (DXA) to confirm osteoporosis was provided to attending physicians in each participating HF.

RESULTS: Over a 12-month period, AI services analyzed 5394 CT scans. CFs and/or reduced vertebral bone density were identified in 1125 patients. Patients with a previously confirmed OP, as well as those who refused or were unable to undergo further testing, were excluded. A total of 66 patients underwent DXA. Age ranged from 54 to 86 years; the median (Q1–Q3) age was 70 (62–74) years; the male to female ratio was 21% and 79%, respectively. According to DXA findings, bone mineral density (BMD) values consistent with OP, osteopenia, and normal BMD were reported in 26 patients (39.4%), 37 patients (56.1%), and 3 patients (4.5%), respectively. Diagnostic performance metrics were calculated for both DXA and CT-based vertebral bone density assessment, with sensitivity of 0.71 vs. 0.91, specificity of 0.80 vs. 0.55, and accuracy of 0.76 vs. 0.67, respectively. Significant differences were observed between osteoporosis, osteopenia, and normal BMD groups, as well as between age-norm groups and those identified by AI services (p < 0.001).

CONCLUSION: The results support the use of AI services for diagnosing OP based on routine CT examinations as part of opportunistic screening.

About the authors

Zlata R. Artyukova

Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Author for correspondence.
Email: zl.artyukova@gmail.com
ORCID iD: 0000-0003-2960-9787
SPIN-code: 7550-2441

MD

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

Nikita D. Kudryavtsev

Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: KudryavtsevND@zdrav.mos.ru
ORCID iD: 0000-0003-4203-0630
SPIN-code: 1125-8637

MD

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

Alexey V. Petraikin

Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: alexeypetraikin@gmail.com
ORCID iD: 0000-0003-1694-4682
SPIN-code: 6193-1656

MD, Dr. Sci. (Medicine), Associate Professor

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

Dmitry S. Semenov

Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: SemenovDS4@zdrav.mos.ru
ORCID iD: 0000-0002-4293-2514
SPIN-code: 2278-7290

Cand. Sci. (Engineering)

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

Anton V. Vladzimirskyy

Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies; Sechenov First Moscow State Medical University (Sechenov University)

Email: VladzimirskijAV@zdrav.mos.ru
ORCID iD: 0000-0002-2990-7736
SPIN-code: 3602-7120

MD, Dr. Sci. (Medicine)

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051; Moscow

Yuriy A. Vasilev

Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: VasilevYA1@zdrav.mos.ru
ORCID iD: 0000-0002-5283-5961
SPIN-code: 4458-5608

MD, Cand. Sci. (Medicine)

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

References

  1. Belaya ZhE, Belova KYu, Biryukova EV, et al. Federal clinical guidelines for diagnosis, treatment and prevention of osteoporosis. Osteoporosis and Bone Diseases. 2021;24(2):4–47. doi: 10.14341/osteo12930 EDN: TUONYE
  2. The International Society For Clinical Densitometry (ISCD). The Adult Official Positions of the ISCD. 2023. Available from: https://iscd.org/official-positions-2023/ Accessed: Apr 18, 2023.
  3. Petryaikin AV, Artyukova ZR, Nizovtsova LA, et al. M 54 Methodological recommendations for conducting dual-energy X-ray absorptiometry. Moscow: GBUZ “NPCC DiT DZM”; 2022. 60 p. (In Russ.).
  4. Alacreu E, Moratal D, Arana E. Opportunistic screening for osteoporosis by routine CT in Southern Europe. Osteoporosis International. 2017;28(3):983–990. doi: 10.1007/s00198-016-3804-3
  5. Gossner J. Missed incidental vertebral compression fractures on computed tomography imaging: More optimism justified. World J Radiol. 2010;21(2):472–473. doi: 10.4329/wjr.v2.i12.472
  6. Carberry GA, Pooler BD, Binkley N, et al. Unreported vertebral body compression fractures at abdominal multidetector CT. Radiology. 2013;268(1):120–126. doi: 10.1148/radiol.13121632
  7. Vasiliev YuA, Vladzimirsky AV. Computer vision in radiation diagnostics: the first stage of the Moscow Experiment. Moscow: Publishing Solution; 2023. (In Russ.).
  8. Pisov M, Kondratenko V, Zakharov A, et al. Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification. Lecture Notes in Computer Science. 2020;12266:723–732. doi: 10.1007/978-3-030-59725-2_70
  9. Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Computers in Biology and Medicine. 2018;98:8–15. doi: 10.1016/j.compbiomed.2018.05.011
  10. Cheng X, Zhao K, Zha X, et al. Opportunistic Screening Using Low-Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study. Journal of Bone and Mineral Research. 2021;36(3):427–435. doi: 10.1002/jbmr.4187
  11. Artificial intelligence services in radiation diagnostics. 2023. Available from: https://mosmed.ai/ Accessed: Apr 18, 2023. (In Russ.).
  12. Genant HK, Wu CY, van Kuijk C, et al. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res. 1993;8(9):1137–48. doi: 10.1002/jbmr.5650080915
  13. Petryaikin AV, Belaya ZhE, Belyaev MG, et al. Accuracy of automatic diagnostics of compression fractures of vertebral bodies according to the morphometric algorithm of artificial intelligence. Osteoporosis and Bone Diseases. 2022;25(3):92–93. (In Russ.). doi: 10.14341/osteo13064
  14. Petraikin AV, Artyukova ZR, Kudryavtsev ND, et al. Analysis of Age Distribution of Bone Mineral Density by Dual-Energy X-Ray Absorptiometry. Journal of Radiology and Nuclear Medicine. 2023;104(1):21–29. doi: 10.20862/0042-4676-2023-104-1-21-29 EDN: ULUKYU
  15. Lesnyak OM, Yershova OB, Zakroeva AG, et al. Audit of the Russian Osteoporosis Association. 2020. Р. 44. (In Russ.).
  16. Salari N, Ghasemi H, Mohammadi L, et al. The global prevalence of osteoporosis in the world: a comprehensive systematic review and meta-analysis. Journal of Orthopaedic Surgery and Research. 2021;16(1):609. doi: 10.1186/s13018-021-02772-0
  17. Murata K, Endo K, Aihara T, et al. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Sci Rep. 2020;10(1):20031. doi: 10.1038/s41598-020-76866-w
  18. Dong Q, Luo G, Lane NE, et al. Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria. Acad Radiol. 2022;29(12):1819–1832. doi: 10.1016/j.acra.2022.02.020
  19. Valentinitsch A, Trebeschi S, Kaesmacher J, et al. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int. 2019;30(6):1275–1285. doi: 10.1007/s00198-019-04910-1
  20. Yasaka K, Akai H, Kunimatsu A, et al. Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. Eur Radiol. 2020;30(6):3549–3557. doi: 10.1007/s00330-020-06677-0
  21. Nam KH, Seo I, Kim DH, et al. Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography. J Korean Neurosurg Soc. 2019;62(4):442–449. doi: 10.3340/jkns.2018.0178
  22. Zhang J, Liu J, Liang Z, et al. Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features. BMC Musculoskeletal Disorders. 2023;24(1):165. doi: 10.1186/s12891-023-06281-5
  23. Certificate of State registration of the database No. 2023621171 Russian Federation. Vasiliev YuA, Turavilova EV, Vladzimirsky AV, et al. MosMedData: CT scan with signs of spinal osteoporosis. The applicant is the State Budgetary Healthcare Institution of the city of Moscow “Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Department of Healthcare of the City of Moscow”. Registration date: 04/11/2023. (In Russ.).
  24. Vasiliev YuA, Vlazimirsky AV, Omelyanskaya OV, et al. Methodology for testing and monitoring artificial intelligence-based software for medical diagnostics. Digital Diagnostics. 2023;4(3):252−267. doi: 10.17816/DD321971 EDN: UEDORU
  25. Bobrovskaya TM, Kirpichev YS, Savkina EF, Chetverikov SF, Arzamasov KM. Development and validation of a tool for statistical comparison of roc-curves using the example of algorithms based on artificial intelligence technologies Medical doctor and information technologies. 2023;3:4–15. doi: 10.25881/18110193_2023_3_4 EDN: CUFICX
  26. Artyukova ZR, Kudryavtsev ND, Petraikin AV, et al. Using an artificial intelligence algorithm to assess the bone mineral density of the vertebral bodies based on computed tomography data. Medical Visualization. 2023;27(2):125–137. doi: 10.24835/1607-0763-1257 EDN: FQACCV
  27. Petraikin AV, Belaya ZhE, Kiseleva AN, et al. Artificial intelligence for diagnosis of vertebral compression fractures using a morphometric analysis model, based on convolutional neural networks. Problems of Endocrinology. 2020;66(5):48–60. doi: 10.14341/probl12605 EDN: GLXSYG
  28. Löffler MT, Jacob A, Scharr A, et al. Automatic opportunistic osteoporosis screening in routine CT: improved prediction of patients with prevalent vertebral fractures compared to DXA. Eur Radiol. 2021;31(8):6069–6077. doi: 10.1007/s00330-020-07655-2
  29. Petraikin AV, Toroptsova NV, Nikitsinskaya OA, et al. Using asynchronous quantitative computed tomography for opportunistic screening of osteoporosis. Rheumatology Science and Practice. 2022;60(3):360–368. doi: 10.47360/1995-4484-2022-360-368 EDN: KTYJHB
  30. Mikhailov EE, Benevolenskaya LI. Epidemiology of osteoporosis and fractures. In: A Guide to Osteoporosis. Moscow: BINOM. Laboratory of Knowledge; 2003: 10–55. (In Russ.).
  31. Morozov SP, Gavrilov AV, Arkhipov IV, et al. Effect of artificial intelligence technologies on the CT scan interpreting time in COVID-19 patients in inpatient setting. Russian Journal of Preventive Medicine. 2022;25(1):14–20. doi: 10.17116/profmed20222501114 EDN: QRZZKS
  32. Vladzymyrskyy AV, Kudryavtsev ND, Kozhikhina DD, et al. Effectiveness of using artificial intelligence technologies for dual descriptions of the results of preventive lung examinations. Russian Journal of Preventive Medicine. 2022;25(7):7–15. doi: 10.17116/profmed2022250717 EDN: JNUMFN

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Schematic representation of the pilot study.

Download (432KB)
Indexing metadata
3. Fig. 2. Example of screening (female, 84 years old): a, additional CT scan series; b, dual-energy X-ray absorptiometry of the lumbar spine and proximal femur. In January 2023, the patient underwent chest CT. The scan was analyzed by an AI service (Genant-IRA), which revealed signs of osteoporosis (compression deformity of the Th12 vertebral body up to 32%, and vertebral body density at Th11, L1, and L2 below 100 HU). The patient was subsequently referred for dual-energy X-ray absorptiometry, which was performed in May 2023.

Download (310KB)
Indexing metadata
4. Fig. 3. Dual-energy X-ray absorptiometry findings.

Download (160KB)
Indexing metadata
5. Fig. 4. Distribution of patients who underwent DXA by sex and bone mineral density.

Download (206KB)
Indexing metadata

Copyright (c) 2025 Eco-Vector

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».