标准医疗日期(MosMedData)独立外部评价的算法在诊断的人工智能基础上

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这篇文章介绍了一个独特的方法来创建附加说明的医疗日期，以测试基于人工智能技术的诊断解决方案。描述了数据集形成的四个阶段-计划，初始数据选择，标记和验证，文档。所举的例子是根据上述日期方法建立的。该方法是广泛而普遍的，因此可以应用于医学和卫生的其他领域，它是由人工智能技术和高数据技术的自动化和发展

作者简介

Nikolay Pavlov

Moscow Center for Diagnostics and Telemedicine

编辑信件的主要联系方式.
Email: n.pavlov@npcmr.ru
ORCID iD: 0000-0002-4309-1868
SPIN 代码: 9960-4160
https://pavlov.rocks
俄罗斯联邦, 28-1, Srednyaya Kalitnikovskaya street, 109029, Moscow

Anna Andreychenko

Moscow Center for Diagnostics and Telemedicine

Email: a.andreychenko@npcmr.ru
ORCID iD: 0000-0001-6359-0763
SPIN 代码: 6625-4186

PhD

俄罗斯联邦, 28-1, Srednyaya Kalitnikovskaya street, 109029, Moscow

Anton Vladzymyrskyy

Moscow Center for Diagnostics and Telemedicine

Email: a.vladzimirsky@npcmr.ru
ORCID iD: 0000-0002-2990-7736
SPIN 代码: 3602-7120

MD, Dr. Sci. (Med.)

俄罗斯联邦, 28-1, Srednyaya Kalitnikovskaya street, 109029, Moscow

Anush Revazyan

Moscow Center for Diagnostics and Telemedicine

Email: anushrevazyan@gmail.com
ORCID iD: 0000-0003-1589-2382
俄罗斯联邦, 28-1, Srednyaya Kalitnikovskaya street, 109029, Moscow

Yury Kirpichev

Moscow Center for Diagnostics and Telemedicine

Email: y.kirpichev@npcmr.ru
ORCID iD: 0000-0002-9583-5187
SPIN 代码: 3362-3428
俄罗斯联邦, 28-1, Srednyaya Kalitnikovskaya street, 109029, Moscow

Sergey Morozov

Moscow Center for Diagnostics and Telemedicine

Email: morozov@npcmr.ru
ORCID iD: 0000-0001-6545-6170
SPIN 代码: 8542-1720

MD, Dr. Sci. (Med.), Professor

俄罗斯联邦, 28-1, Srednyaya Kalitnikovskaya street, 109029, Moscow

参考

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  3. Griffith B, Kadom N, Straus CM. Radiology Education in the 21st Century: Threats and Opportunities. J Am Coll Radiol. 2019;16(10):1482–1487. doi: 10.1016/j.jacr.2019.04.003
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  8. Kohli MD, Summers RM, Geis JR. Medical image data and datasets in the era of machine learning—whitepaper from the 2016 C- MIMI meeting dataset session. J Digit Imaging. 2017;30(4):392–399. doi: 10.1007/s10278-017-9976-3
  9. Willemink MJ, Koszek WA, Hardell C, et al. Preparing medical imaging data for machine learning. Radiology. 2020;295(1):4–15. doi: 10.1148/radiol.2020192224
  10. Morozov SP, Shelekhov PV, Vladzymyrsky AV. Modern approaches to the radiology service improvement. Health Care Standardization Problems. 2019;(5-6):30−34. (In Russ). doi: 10.26347/1607-2502201905-06030-034
  11. Kulberg NS, Gusev MA, Reshetnikov RV, et al. Methodology and tools for creating training samples for artificial intelligence systems for recognizing lung cancer on CT images. Health Care Russian Federation. 2021;64(6):343–350. doi: 10.46563/0044-197x-2020-64-6-343-350
  12. Preston-Werner T. Semantic Versioning 2.0.0 [Internet]. Available from: https://semver.org
  13. Morozov SP, Protsenko DN, Smetanina SV, et al. Radiation diagnostics of coronavirus disease (COVID-19): organization, methodology, interpretation of results: Preprint No.CDT ― 2020 ― II. Version 2 from 17.04.2020. The series “Best practices of radiation and instrumental diagnostics”. Issue 65. Moscow : Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Department of Health; 2020. 80 p. (In Russ). Avalable from: https://tele-med.ai/biblioteka-dokumentov/luchevaya-diagnostika-koronavirusnoj-bolezni-covid-19-organizaciya-metodologiya-interpretaciya-rezultatov
  14. Pavlov N. ECR 2021: Value of technical stratification of medical datasets for AI services. Moscow, 2021. [Internet]. Available from: https://connect.myesr.org/course/ai-in-breast-imaging/
  15. Morozov SP, Vladzymyrskyy A, Andreychenko A, et al. Moscow experiment on computer vision in radiology: involvement and participation of radiologists. Vrach i informacionnye tehnologii. 2020;(4):14–23. doi: 10.37690/1811-0193-2020-4-14-23
  16. Morozov SP, Vladzymyrskyy AV, Klyashtornyy VG, et al. Clinical acceptance of software based on artificial intelligence technologies (radiology). Series “Best practices in medical imaging”. Issue 57. Moscow; 2019. 45 p.
  17. Morozov SP, Andreychenko AE, Pavlov NA, et al. MosMedData: Chest CT scans with COVID-19 related findings dataset. medRxiv. 2020. doi: 10.1101/2020.05.20.20100362
  18. Sushentsev N, Bura V, Kotniket M, et al. A head-to-head comparison of the intra- and interobserver agreement of COVID-RADS and CO-RADS grading systems in a population with high estimated prevalence of COVID-19. BJR Open. 2020;2(1):20200053. doi: 10.1259/bjro.20200053
  19. Jin C, Chen W, Caoet Y, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun. 2020;11(1):5088. doi: 10.1038/s41467-020-18685-1

补充文件

附件文件
动作
1. JATS XML
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2. Fig. 1. Stages of forming a medical dataset.

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3. Fig. 2. The relationship of the clinical task, dataset and success in the implementation of a solution based on artificial intelligence (AI) in routine clinical practice.

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4. Fig. 3. Datasets of the Moscow experiment on the use of innovative technologies in the field of computer vision for the analysis of medical images and further use in the health care system of Moscow, prepared according to this method.

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5. Fig. 4. Classification of markup by labor costs and degree of verification.

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6. Fig. 5. Basic structure of the README file.

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7. 图 1形成医疗数据集的阶段。

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8. 图 2临床任务，数据集与常规临床实践中实施人工智能（AI）解决方案的成功之间的关系。

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9. 图 3莫斯科的数据集实验了根据此方法在计算机视觉领域中使用创新技术来分析医学图像并进一步在莫斯科的医疗 保健系统中使用。

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10. 图 4按劳动成本和核查程度对标记进行分类。

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11. 图 5README文件的基本结构。

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版权所有 © Pavlov N., Andreychenko A., Vladzymyrskyy A., Revazyan A., Kirpichev Y., Morozov S., 2021

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。

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