ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2025-25-5-844-855ntv-514Research ArticleКОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИCOMPUTER SCIENCEУниверсальная модель архитектуры краудсорсинговой системы разметки и подготовки медицинских данныхA universal architecture model of a crowdsourcing medical data labeling system designedhttps://orcid.org/0009-0007-8233-4387КоваленкоЛ. А.KovalenkoL. A.

Коваленко Лев Алексеевич — ведущий программист; ведущий программист

sc 59225183700

Санкт-Петербург, 199034

Санкт-Петербург, 197101

Lev A. Kovalenko — Leading Software Developer; Leading Software Developer

sc  59225183700

Saint Petersburg, 199034

Saint Petersburg, 197101

levozavr1@gmail.comhttps://orcid.org/0000-0002-7305-1429БлекановИ. С.BlekanovI. S.

Блеканов Иван Станиславович — кандидат технических наук, доцент, заведующий кафедрой

sc 56149559700

Санкт-Петербург, 199034

Ivan. S. Blekanov — PhD, Associate Professor, Head of Department

sc 56149559700

Saint Petersburg, 199034

i.blekanov@spbu.ruhttps://orcid.org/0009-0007-1468-0042ЕжовФ. В.EzhovF. V.

Ежов Федор Валерьевич — инженер-программист

sc 59224591300

Санкт-Петербург, 199034

Fedor V. Ezhov — Software Developer - Engineer

sc 59224591300

Saint Petersburg, 199034

st056053@student.spbu.ruhttps://orcid.org/0009-0007-6199-3607ЛаринЕ. С.LarinE. S.

Ларин Евгений Сергеевич — ведущий аналитик

Санкт-Петербург, 199034

Evgenii S. Larin — Leading Analyst

Saint Petersburg, 199034

evgeny.larin@spbu.ruhttps://orcid.org/0000-0002-9344-5724КимГ. И.KimG. I.

Ким Глеб Ирламович — кандидат медицинских наук, врач сердечно-сосудистый хирург; доцент

sc 57704764600

Санкт-Петербург, 190020

Санкт-Петербург, 199034

Gleb I. Kim — PhD (Medicine), Cardiovascular Surgeon; Associate Professor

sc 57704764600

Saint Petersburg, 199034

Saint Petersburg, 190020

gikim.cor@gmail.comСанкт-Петербургский государственный университет; Университет ИТМОРоссияSt. Petersburg State University (SPbSU); ITMO UniversityRussian FederationСанкт-Петербургский государственный университетРоссияSt. Petersburg State University (SPbSU)Russian FederationСанкт-Петербургский государственный университет; Санкт-Петербургский государственный университет, Клиника высоких медицинских технологий им. Н.И. ПироговаРоссияSt. Petersburg State University (SPbSU); Saint Petersburg State University HospitalRussian Federation202527102025255844855Copyright © Коваленко Л.А., Блеканов И.С., Ежов Ф.В., Ларин Е.С., Ким Г.И., 20252025Коваленко Л.А., Блеканов И.С., Ежов Ф.В., Ларин Е.С., Ким Г.И.Kovalenko L.A., Blekanov I.S., Ezhov F.V., Larin E.S., Kim G.I.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/514

конкретной области медицины, а также наличие специализированных инструментов, упрощающих данный процесс и учитывающих специфику обработки медицинских данных. Метод. В работе предложена универсальная архитектурная модель краудсорсинговой системы, специализированной для разметки медицинских данных. Модель поддерживает обработку различных медицинских форматов данных, имеет механизмы анонимизации и многоуровневого контроля качества, позволяет организовать распределенный процесс разметки с привлечением экспертного сообщества. Основные результаты. Приведена классификация актуальных проблем процесса сбора и разметки медицинских данных, сформулированы критерии качества и безопасности для сравнительного анализа систем разметки медицинских данных. Предложена схема обобщенного сценария взаимодействия групп пользователей с краудсорсинговой системой в контексте решения задач искусственного интеллекта в области медицины. Построена универсальная модель архитектуры такой системы. На ее основе реализована специализированная краудсорсинговая система разметки медицинских данных на базе Computer Vision Annotation Tool. Проведено тестирование и апробация реализованной системы кардиохирургами Клиники высоких медицинских технологий им. Н.И. Пирогова Санкт-Петербургского государственного университета. Обсуждение. Предложенная модель архитектуры краудсорсинговой системы может быть использована для повышения эффективности и безопасности организации и построения процесса разметки медицинских данных пациентов при решении различных прикладных задач машинного обучения/искусственного интеллекта, таких как семантическая сегментация внутренних органов и их патологий, детекция и классификация заболеваний по медицинским снимкам (например, компьютерной томографии). Разработанное решение может использоваться врачами различной специализации, исследователями и разработчиками, направленными на развитие и создание методов и технологий искусственного интеллекта в области медицины.

Machine Learning (ML) and Artificial Intelligence (AI) methods are used to process and intelligently analyze medical data. The application of ML/AI methods requires specialized sets of labeled medical data of large dimensions. Process organization of quality medical data labeling requires the involvement of a large number assessors and specialists in a particular field of medicine as well as the availability of specialized tools for labeling process optimization considering the specifics of medical data processing. In this paper a universal architectural model of a crowdsourcing system specifically designed for medical data labeling was proposed. The model supports processing of diverse medical data formats, incorporates data anonymization mechanisms and multi-level quality control, while enabling a distributed annotation process with expert community involvement. As a result, classification of actual problems of the process of medical data labeling and data collection, and a quality and safety criteria for comparative analysis of medical data labeling systems was detected and formulated. The scheme of generalized scenario of users’ groups interaction with crowdsourcing system in the context of solving AI problems in the field of medicine was proposed. A universal model of such system architecture was designed and a specialized crowdsourcing system of medical data labeling based on Computer Vision Annotation Tool was implemented on its basis. Testing and approbation of the realized system was carried out at the Pirogov Clinic of High Medical Technologies. The proposed universal model of crowdsourcing system architecture can be used to improve the efficiency and safety of organization and construction of the process of labeling patients’ medical data in the context of solving various applied ML/AI tasks, such as semantic segmentation of internal organs and their pathologies, detection and classification of diseases based on medical images (e.g. computed tomography scans). The developed solution can be used by doctors of various specializations, researchers and developers aimed at the development and creation of methods and technologies of AI in the field of medicine.

краудсорсингразметка медицинских данныхмодель программной архитектурыкритерии качества систем краудсорсингаобработка медицинских данныхпользовательский сценарий взаимодействияcrowdsourcingmedical data annotationsoftware architecture modelquality criteria for crowdsourcing systemsmedical data processinguse caseReferencesTopol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019. 341 p.Topol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019, 341 p.Obermeyer Z., Emanuel E.J. Predicting the future — big data, machine learning, and clinical medicine // New England Journal of Medicine. 2016. V. 375. N 13. P. 1216–1219. https://doi.org/10.1056/nejmp1606181Obermeyer Z., Emanuel E.J. Predicting the future — big data, machine learning, and clinical medicine. New England Journal of Medicine, 2016, vol. 375, no. 13, pp. 1216–1219. https://doi.org/10.1056/nejmp1606181Jiang F., Jiang Y., Zhi H., Dong Y., Li H., Ma S., et al. Artificial intelligence in healthcare: past, present and future // Stroke and Vascular Vascular Neurology. 2017. V. 2. N 4. P. 230–243. https://doi.org/10.1136/svn-2017-000101Jiang F., Jiang Y., Zhi H., Dong Y., Li H., Ma S., et al. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Vascular Neurology. 2017. V. 2. N 4. P. 230–243. https://doi.org/10.1136/svn-2017-000101Secinaro S., Calandra D., Secinaro A., Muthurangu V., Biancone P. The role of artificial intelligence in healthcare: a structured literature review // BMC Medical Informatics and Decision Making. 2021. V. 21. N 1. P. 125. https://doi.org/10.1186/s12911-021-01488-9Secinaro S., Calandra D., Secinaro A., Muthurangu V., Biancone P. The role of artificial intelligence in healthcare: a structured literature review // BMC Medical Informatics and Decision Making. 2021. V. 21. N 1. P. 125. https://doi.org/10.1186/s12911-021-01488-9Roh Y., Heo G., Whang S.E. A survey on data collection for machine learning: a big data – Al Integration perspective // IEEE Transactions on Knowledge and Data Engineering. 2021. V. 33. N 4. P. 1328–1347. https://doi.org/10.1109/TKDE.2019.2946162Roh Y., Heo G., Whang S.E. A survey on data collection for machine learning: a big data – Al Integration perspective // IEEE Transactions on Knowledge and Data Engineering. 2021. V. 33. N 4. P. 1328–1347. https://doi.org/10.1109/TKDE.2019.2946162Апанасович К.С., Махныткина О.В., Кабаров В.И., Далевская О.П. RuPersonaChat: корпус диалогов для персонификации разговорных агентов // Научно-технический вестник информационных технологий, механики и оптики. 2024. Т. 24. № 2. С. 214–221. https://doi.org/10.17586/2226-1494-2024-24-2-214-221Apanasovich K.S., Makhnytkina O.V., Kabarov V.I., Dalevskaya O.P. RuPersonaChat: a dialog corpus for personalizing conversational agents. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2024, vol. 24, no. 2, pp. 214–221. (in Russian). https://doi.org/10.17586/2226-1494-2024-24-2-214-221Shaheen Z., Mouromtsev D.I., Postny I. RuLegalNER: a new dataset for Russian legal named entities recognition. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2023. V. 23. N 4. P. 854–857. https://doi.org/10.17586/2226-1494-2023-23-4-854-857Shaheen Z., Mouromtsev D.I., Postny I. RuLegalNER: a new dataset for Russian legal named entities recognition. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 4, pp. 854–857. https://doi.org/10.17586/2226-1494-2023-23-4-854-857Sayin B., Krivosheev E., Yang J., Passerini A., Casati F. A review and experimental analysis of active learning over crowdsourced data // Artificial Intelligence Review. 2021. V. 54. N 7. P. 5283–5305. https://doi.org/10.1007/s10462-021-10021-3Sayin B., Krivosheev E., Yang J., Passerini A., Casati F. A review and experimental analysis of active learning over crowdsourced data. Artificial Intelligence Review, 2021, vol. 54, no. 7, pp. 5283–5305. https://doi.org/10.1007/s10462-021-10021-3Xintong G., Hongzhi W., Song Y., Hong G. Brief survey of crowdsourcing for data mining // Expert Systems With Application. 2014. V. 41. N 17. P. 7987–7994. https://doi.org/10.1016/j.eswa.2014.06.044Xintong G., Hongzhi W., Song Y., Hong G. Brief survey of crowdsourcing for data mining. Expert Systems With Application, 2014, vol. 41, no. 17, pp. 7987–7994. https://doi.org/10.1016/j.eswa.2014.06.044Hecht R., Kalla M., Krüger T. Crowd-sourced data collection to support automatic classification of building footprint data // Proc. of the ICA. 2018. V. 1. P. 54. https://doi.org/10.5194/ica-proc-1-54-2018Hecht R., Kalla M., Krüger T. Crowd-sourced data collection to support automatic classification of building footprint data. Proc. of the ICA, 2018, vol. 1, pp. 54. https://doi.org/10.5194/ica-proc-1-54-2018Mnih V., Kavukcuoglu K., Silver D., Rusu A.A., Veness J., Bellemare M.G., et al. Human-level control through deep reinforcement learning // Nature. 2015. V. 518. N 7540. P. 529–533. https://doi.org/10.1038/nature14236Mnih V., Kavukcuoglu K., Silver D., Rusu A.A., Veness J., Bellemare M.G., et al. Human-level control through deep reinforcement learning. Nature, 2015, vol. 518, no. 7540, pp. 529–533. https://doi.org/10.1038/nature14236Rahmani A.M., Yousefpoor E., Yousefpoor M.S., Mehmood Z., Haider A., Hosseinzadeh M., Naqvi R.A. Machine learning (ML) in medicine: review, applications, and challenges // Mathematics. 2021. V. 9. N 22. P. 2970. https://doi.org/10.3390/math9222970Rahmani A.M., Yousefpoor E., Yousefpoor M.S., Mehmood Z., Haider A., Hosseinzadeh M., Naqvi R.A. Machine learning (ML) in medicine: review, applications, and challenges. Mathematics, 2021, vol. 9, no. 22, pp. 2970. https://doi.org/10.3390/math9222970Wang C., Han L., Stein G., Day S., Bien-Gund C, Mathews A., et al. Crowdsourcing in health and medical research: a systematic review // Infectious Diseases of Poverty. 2020. V. 9. N 1. P. 8. https://doi.org/10.1186/s40249-020-0622-9Wang C., Han L., Stein G., Day S., Bien-Gund C, Mathews A., et al. Crowdsourcing in health and medical research: a systematic review. Infectious Diseases of Poverty, 2020, vol. 9, no. 1, pp. 8. https://doi.org/10.1186/s40249-020-0622-9Ellis R.J., Sander R.M., Limon A. Twelve key challenges in medical machine learning and solutions // Intelligence-Based Medicine. 2022. V. 6. P. 100068. https://doi.org/10.1016/j.ibmed.2022.100068Ellis R.J., Sander R.M., Limon A. Twelve key challenges in medical machine learning and solutions. Intelligence-Based Medicine, 2022, vol. 6, pp. 100068. https://doi.org/10.1016/j.ibmed.2022.100068Xia H., McKernan B. Privacy in crowdsourcing: a review of the threats and challenges // Computer Supported Cooperative Work (CSCW). 2020. V. 29. N 3. P. 263–301. https://doi.org/10.1007/s10606-020-09374-0Xia H., McKernan B. Privacy in crowdsourcing: a review of the threats and challenges. Computer Supported Cooperative Work (CSCW), 2020, vol. 29, no. 3, pp. 263–301. https://doi.org/10.1007/s10606-020-09374-0Rother A., Niemann U., Hielscher T., Völzke H., Ittermann T., Spiliopoulou M. Assessing the difficulty of annotating medical data in crowdworking with help of experiments // PLOS ONE. 2021. V. 16. N 7. P. e0254764. https://doi.org/10.1371/journal.pone.0254764Rother A., Niemann U., Hielscher T., Völzke H., Ittermann T., Spiliopoulou M. Assessing the difficulty of annotating medical data in crowdworking with help of experiments. PLOS ONE, 2021, vol. 16, no. 7, pp. e0254764. https://doi.org/10.1371/journal.pone.0254764Ye C., Coco J., Epishova A., Hajaj C., Bogardus H., Novak L., et al. A crowdsourcing framework for medical data sets // AMIA Joint Summits on Translational Science proceedings. 2018. P. 273–280.Ye C., Coco J., Epishova A., Hajaj C., Bogardus H., Novak L., et al. A crowdsourcing framework for medical data sets. AMIA Joint Summits on Translational Science proceedings, 2018, pp. 273–280.Kittur A., Nickerson J., Bernstein M., Gerber E., Shaw A., Zimmerman J., et al. The future of crowd work // Proc. of the Conference on Computer Supported Cooperative Work. 2013. P. 1301–1318. https://doi.org/10.1145/2441776.2441923Kittur A., Nickerson J., Bernstein M., Gerber E., Shaw A., Zimmerman J., et al. The future of crowd work. Proc. of the Conference on Computer Supported Cooperative Work, 2013, pp. 1301–1318. https://doi.org/10.1145/2441776.2441923Ørting S.N., Doyle A., van Hilten A., Hirth M., Inel O., Madan C.R., et al. A survey of crowdsourcing in medical image analysis // Human Computation. 2020. V. 7. N 1. P. 1–26. https://doi.org/10.15346/hc.v7i1.1Ørting S.N., Doyle A., van Hilten A., Hirth M., Inel O., Madan C.R., et al. A survey of crowdsourcing in medical image analysis. Human Computation, 2020, vol. 7, no. 1, pp. 1–26. https://doi.org/10.15346/hc.v7i1.1Lu J., Li W., Wang Q., Zhang Y. Research on data quality control of crowdsourcing annotation: a survey // Proc. of the IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). 2020. P. 201– 208. https://doi.org/10.1109/DASC-PICom-CBDComCyberSciTech49142.2020.00044Lu J., Li W., Wang Q., Zhang Y. Research on data quality control of crowdsourcing annotation: a survey. Proc. of the IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2020, pp. 201– 208. https://doi.org/10.1109/DASC-PICom-CBDComCyberSciTech49142.2020.00044Lu X., Ratcliffe D., Kao T.-T., Tikhonov A., Litchfield L., Rodger C., Wang K. Rethinking quality assurance for crowdsourced multi-ROI Computation and Crowdsourcing. 2023. V. 11. N 1. P. 103–114. https://doi.org/10.1609/hcomp.v11i1.27552Lu X., Ratcliffe D., Kao T.-T., Tikhonov A., Litchfield L., Rodger C., Wang K. Rethinking quality assurance for crowdsourced multi-ROI image segmentation // Proc. of the 11th AAAI Conference on Human Neurology, 2017, vol. 2, no. 4, pp. 230–243. https://doi.org/10.1136/svn-2017-000101Тесленко Е.В. Искусственный интеллект в медицине. Правовые аспекты // Наука молодых — будущее России: сборник научных статей 8-й Международной научной конференции перспективных разработок молодых ученых. Курск: Университетская книга, 2023. С. 435–438.Teslenko E.V. Artificial intelligence in medicine. Legal aspects. Proc. of the Science of the young — the future of Russia. 2023. pp. 435–438. (in Russian)Hulsen T. Sharing is caring — data sharing initiatives in healthcare // International Journal of Environmental Research and Public Health. 2020. V. 17. N 9. P. 3046. https://doi.org/10.3390/ijerph17093046Hulsen T. Sharing is caring — data sharing initiatives in healthcare. International Journal of Environmental Research and Public Health, 2020, vol. 17, no. 9, pp. 3046. https://doi.org/10.3390/ijerph17093046Sims M.H., Shaw M.H., Gilbertson S., Storch J., Halterman M.W. Legal and ethical issues surrounding the use of crowdsourcing among healthcare providers // Health Informatics Journal. 2019. V. 25. N 4. P. 1618–1630. https://doi.org/10.1177/1460458218796599Sims M.H., Shaw M.H., Gilbertson S., Storch J., Halterman M.W. Legal and ethical issues surrounding the use of crowdsourcing among healthcare providers. Health Informatics Journal, 2019, vol. 25, no. 4, pp. 1618–1630. https://doi.org/10.1177/1460458218796599Mason W., Suri S. Conducting behavioral research on Amazon’s Mechanical Turk // Behavior Research Methods. 2012. V. 44. N 1. P. 1–23. https://doi.org/10.3758/s13428-011-0124-6Mason W., Suri S. Conducting behavioral research on Amazon’s Mechanical Turk. Behavior Research Methods, 2012, vol. 44, no. 1, pp. 1–23. https://doi.org/10.3758/s13428-011-0124-6Buecheler T., Sieg J.H., Füchslin R.M., Pfeifer R. Crowdsourcing, open innovation and collective intelligence in the scientific method: a research agenda and operational framework // Proc. of the 12th International Conference on the Synthesis and Simulation of Living Systems. 2010. P. 679–686.Buecheler T., Sieg J.H., Füchslin R.M., Pfeifer R. Crowdsourcing, open innovation and collective intelligence in the scientific method: a research agenda and operational framework. Proc. of the 12th International Conference on the Synthesis and Simulation of Living Systems, 2010, pp. 679–686.Dortheimer J. Collective intelligence in design crowdsourcing // Mathematics. 2022. V. 10. N 4. P. 539. https://doi.org/10.3390/math10040539Dortheimer J. Collective intelligence in design crowdsourcing. Mathematics, 2022, vol. 10, no. 4, pp. 539. https://doi.org/10.3390/math10040539Le K.H., Tran T.V., Pham H.H., Nguyen H.T., Le T.T., Nguyen H. Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis // IEEE Access. 2023. V. 11. P. 14105–14114. https://doi.org/10.1109/ACCESS.2023.3243845Le K.H., Tran T.V., Pham H.H., Nguyen H.T., Le T.T., Nguyen H. Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis. IEEE Access, 2023, vol. 11, pp. 14105–14114. https://doi.org/10.1109/ACCESS.2023.3243845Petrović N., Moyà-Alcover G., Varona J., Jaume-i-Capó A. Crowdsourcing human-based computation for medical image analysis: a systematic literature review // Health Informatics Journal. 2 0 2 0 . V. 2 6 . N 4 . P. 2 4 4 6 – 2 4 6 9 .https://doi.org/10.1177/1460458220907435Petrović N., Moyà-Alcover G., Varona J., Jaume-i-Capó A. Crowdsourcing human-based computation for medical image analysis: a systematic literature review. Health Informatics Journal, 2020, vol. 26, no. 4, pp. 2446–2469. https://doi.org/10.1177/1460458220907435Vindas Y., Guépié B.K., Almar M., Roux E., Delachartre P. Semiautomatic data annotation based on feature-space projection and local quality metrics: An application to cerebral emboli characterization // Medical Image Analysis. 2022. V. 79. P. 102437. https://doi.org/10.1016/j.media.2022.102437Vindas Y., Guépié B.K., Almar M., Roux E., Delachartre P. Semiautomatic data annotation based on feature-space projection and local quality metrics: An application to cerebral emboli characterization. Medical Image Analysis, 2022, vol. 79, pp. 102437. https://doi.org/10.1016/j.media.2022.102437Philbrick K. A., Weston A.D., Akkus Z., Kline T.L., Korfiatis P., Sakinis T., et al. RIL-Contour: a medical imaging dataset annotation tool for and with deep learning // Journal of Digital Imaging. 2019. V. 32. N 4. P. 571–581. https://doi.org/10.1007/s10278-019-00232-0Philbrick K. A., Weston A.D., Akkus Z., Kline T.L., Korfiatis P., Sakinis T., et al. RIL-Contour: a medical imaging dataset annotation tool for and with deep learning. Journal of Digital Imaging, 2019, vol. 32, no. 4, pp. 571–581. https://doi.org/10.1007/s10278-019-00232-0Li H., Zhang B., Zhang Y., Liu W.W., Mao Y.J., Huang J.C., Wei L.F. A semi-automated annotation algorithm based on weakly supervised learning for medical images // Biocybernetics and Biomedical Engineering. 2020. V. 40. N 2. P. 787–802. https://doi.org/10.1016/j.bbe.2020.03.005Li H., Zhang B., Zhang Y., Liu W.W., Mao Y.J., Huang J.C., Wei L.F. A semi-automated annotation algorithm based on weakly supervised learning for medical images. Biocybernetics and Biomedical Engineering, 2020, vol. 40, no. 2, pp. 787–802. https://doi.org/10.1016/j.bbe.2020.03.005Larobina M., Murino L. Medical image file formats // Journal of Digital Imaging. 2014. V. 27. N 2. P. 200–206. https://doi.org/10.1007/s10278-013-9657-9Larobina M., Murino L. Medical image file formats. Journal of Digital Imaging, 2014, vol. 27, no. 2, pp. 200–206. https://doi.org/10.1007/s10278-013-9657-9Willemink M.J., Koszek W.A., Hardell C., Wu J., Fleischmann D., Harvey H., et al. Preparing medical imaging data for machine learning // Radiology. 2020. V. 295. N 1. P. 4–15. https://doi.org/10.1148/radiol.2020192224Willemink M.J., Koszek W.A., Hardell C., Wu J., Fleischmann D., Harvey H., et al. Preparing medical imaging data for machine learning. Radiology, 2020, vol. 295, no. 1, pp. 4–15. https://doi.org/10.1148/radiol.2020192224Pfob A., Lu S.-C., Sidey-Gibbons C. Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison // BMC Medical Research Methodology. 2022. V. 22. N 1. P. 282. https://doi.org/10.1186/s12874-022-01758-8Pfob A., Lu S.-C., Sidey-Gibbons C. Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Medical Research Methodology, 2022, vol. 22, no. 1, pp. 282. https://doi.org/10.1186/s12874-022-01758-8Кондратенко С.С., Коржук В.М. Архитектура системы обработки медицинских данных с учетом требований обеспечения целостности. Сборник тезисов докладов конгресса молодых ученых. 2023. [Электронный ресурс]. URL: https://kmu.itmo.ru/digests/article/11444Kondratenko S.S., Korzhuk V.M. Architecture of a medical data processing system taking into account integrity requirements. Collection of abstracts from the Congress of Young Scientists. 2023. Available at: https://kmu.itmo.ru/digests/article/11444 (in Russian)Васильев Ю.А., Савкина Е.Ф., Владзимирский А.В., Омелянская О.В., Арзамасов К.М. Обзор современных средств разметки цифровых диагностических изображений // Казанский медицинский журнал. 2023. Т. 104. № 5. С. 750–760. https://doi.org/10.17816/KMJ349060Vasilev Y.A., Savkina E.F., Vladzimirskii A.V., Omelianskaia O.V., Arzamasov K.M. Overview of modern digital diagnostic image markup tools. Kazan Medical Journal, 2023, vol. 104, no. 5, pp. 750–760. (in Russian). https://doi.org/10.17816/KMJ349060Ежов Ф.В., Коваленко Л.А., Разумилов Е.С., Блеканов И.С. Инструменты краудсорсинга для анализа и обработки медицинских изображений в виде снимков КТ // Процессы управления и устойчивость. 2023. Т. 10. № 1. C. 291–297.Ezhov F.V., Kovalenko L.A., Razumilov E.S., Blekanov I.S. Crowdsourcing tools for the analysis and processing of medical CT images. Processy Upravlenija i Ustojchivost’, 2023, vol. 10, no. 1, pp. 291–297. (in Russian)Saltz J.S., Krasteva I. Current approaches for executing big data science projects — a systematic literature review // PeerJ Computer Science. 2022. V. 8. P. e862. https://doi.org/10.7717/peerj-cs.862Saltz J.S., Krasteva I. Current approaches for executing big data science projects — a systematic literature review. PeerJ Computer Science, 2022, vol. 8, pp. e862. https://doi.org/10.7717/peerj-cs.862Saltz J.S. CRISP-DM for data science: strengths, weaknesses and potential next steps // Proc. of the IEEE International Conference on Big Data. 2021. P. 2337–2344. https://doi.org/10.1109/bigdata52589.2021.9671634Saltz J.S. CRISP-DM for data science: strengths, weaknesses and potential next steps. Proc. of the IEEE International Conference on Big Data, 2021, pp. 2337–2344. https://doi.org/10.1109/bigdata52589.2021.9671634Saltz J., Hotz N. Factors that influence the selection of a data science process management methodology: an exploratory study // Proc. of the 54th Hawaii International Conference on System Sciences. 2021. P. 949–958. https://doi.org/10.24251/hicss.2021.116Saltz J., Hotz N. Factors that influence the selection of a data science process management methodology: an exploratory study. Proc. of the 54th Hawaii International Conference on System Sciences, 2021, pp. 949–958. https://doi.org/10.24251/hicss.2021.116Zhao X., Zhang P., Song F., Fan G.D., Sun Y.Y., Wang Y.J., et al. D2A U-Net: Automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution // Computers in Biology and Medicine. 2021. V. 135. P. 104526. https://doi.org/10.1016/j.compbiomed.2021.104526Zhao X., Zhang P., Song F., Fan G.D., Sun Y.Y., Wang Y.J., et al. D2A U-Net: Automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution. Computers in Biology and Medicine, 2021, vol. 135, pp. 104526. https://doi.org/10.1016/j.compbiomed.2021.104526Xie Y., Padgett J., Biancardi A.M., Reeves A.P. Automated aorta segmentation in low-dose chest CT images // International Journal of Computer Assisted Radiology and Surgery. 2014. V. 9. N 2. P. 211–219. https://doi.org/10.1007/s11548-013-0924-5Xie Y., Padgett J., Biancardi A.M., Reeves A.P. Automated aorta segmentation in low-dose chest CT images. International Journal of Computer Assisted Radiology and Surgery, 2014, vol. 9, no. 2, pp. 211–219. https://doi.org/10.1007/s11548-013-0924-5Ким Г.И., Блеканов И.С., Ежов Ф.В., Коваленко Л.А., Ларин Е.С., Разумилов Е.С. [и др.] Методы искусственного интеллекта в сердечно-сосудистой хирургии и диагностика патологии аорты и аортального клапана (обзор литературы) // Сибирский журнал клинической и экспериментальной медицины. 2024. Т. 39. № 2. С. 36–45. https://doi.org/10.29001/2073-8552-2024-39-2-36-45Kim G.I., Blekanov I.S., Ezhov F.V., Kovalenko L.A., Larin E.S., Razumilov E.S., et al. Artificial intelligence methods in cardiovascular surgery and diagnosis of pathology of the aorta and aortic valve (literature review). Siberian Journal of Clinical and Experimental Medicine, 2024, vol. 39, no. 2, pp. 36–45. (in Russian). https://doi.org/10.29001/2073-8552-2024-39-2-36-45Gao R., Zhao S., Aishanjiang K., Cai H., Wei T., Zhang Y.C., et al. Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data // Journal of Hematology & Oncology. 2021. V. 14. N 1. P. 154. https://doi.org/10.1186/s13045-021-01167-2Gao R., Zhao S., Aishanjiang K., Cai H., Wei T., Zhang Y.C., et al. Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data. Journal of Hematology & Oncology, 2021, vol. 14, no. 1, pp. 154. https://doi.org/10.1186/s13045-021-01167-2Chen P.-T., Wu T.H., Wang P.C., Chang D.W., Liu K.L., Wu M.S., et al. Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study // Radiology. 2023. V. 306. N 1. P. 172–182. https://doi.org/10.1148/radiol.220152Chen P.-T., Wu T.H., Wang P.C., Chang D.W., Liu K.L., Wu M.S., et al. Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study. Radiology, 2023, vol. 306, no. 1. pp. 172–182. https://doi.org/10.1148/radiol.220152Zhou H., Li L., Liu Z., Zhao K., Chen X., Lu M., et al. Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images // European Radiology. 2021. V. 31. N 6. P. 3931–3940. https://doi.org/10.1007/s00330-020-07454-9Zhou H., Li L., Liu Z., Zhao K., Chen X., Lu M., et al. Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images. European Radiology, 2021, vol. 31, no. 6, pp. 3931–3940. https://doi.org/10.1007/s00330-02007454-9

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