Digital DiagnosticsDigital DiagnosticsDigital DiagnosticsDigital Diagnostics2712-84902712-8962Eco-Vector62425010.17816/DD624250Original Study ArticlesОригинальные исследования原创性科研成果Research ArticleExperience with artificial intelligence algorithms for the diagnosis of vertebral compression fractures based on computed tomography: from testing to practical evaluationОпыт применения сервисов искусственного интеллекта для диагностики компрессионных переломов тел позвонков по данным компьютерной томографии: от тестирования до апробации人工智能在椎体压缩性骨折诊断中计算机断层扫描数据的应用经验：从测试到验证https://orcid.org/0000-0003-2960-97877550-2441ArtyukovaZlata R.АртюковаЗлата РомановнаArtyukovaZlata R.
Russian Federation
zl.artyukova@gmail.comhttps://orcid.org/0000-0003-1694-46826193-1656PetraikinAlexey V.ПетряйкинАлексей ВладимировичPetraikinAlexey V.
Russian Federation

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

д-р мед. наук, доцент

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

alexeypetraikin@gmail.comhttps://orcid.org/0000-0003-4203-06301125-8637KudryavtsevNikita D.КудрявцевНикита ДмитриевичKudryavtsevNikita D.
Russian Federation
KudryavtsevND@zdrav.mos.ruhttps://orcid.org/0000-0001-6923-38397803-1005PetryaykinFedor A.ПетряйкинФедор АлексеевичPetryaykinFedor A.
Russian Federation
feda.petraykin@gmail.comhttps://orcid.org/0000-0002-4293-25142278-7290SemenovDmitry S.СемёновДмитрий СергеевичSemenovDmitry S.
Russian Federation

Cand. Sci. (Engineering)

канд. техн. наук

Cand. Sci. (Engineering)

semenovds4@zdrav.mos.ruhttps://orcid.org/0000-0001-5792-39121811-7595SharovaDaria E.ШароваДарья ЕвгеньевнаSharovaDaria E.
Russian Federation
SharovaDE@zdrav.mos.ruhttps://orcid.org/0000-0002-6674-64414746-7173BelayaZhanna E.БелаяЖанна ЕвгеньевнаBelayaZhanna E.
Russian Federation

MD, Dr. Sci. (Medicine)

д-р мед. наук

MD, Dr. Sci. (Medicine)

jannabelaya@gmail.comhttps://orcid.org/0000-0002-2990-77363602-7120VladzimirskyyAnton V.ВладзимирскийАнтон ВячеславовичVladzimirskyyAnton V.
Russian Federation

MD, Dr. Sci. (Medicine)

д-р мед. наук

MD, Dr. Sci. (Medicine)

VladzimirskijAV@zdrav.mos.ruhttps://orcid.org/0000-0002-0208-52184458-5608VasilevYuriy A.ВасильевЮрий АлександровичVasilevYuriy A.
Russian Federation

MD, Cand. Sci. (Medicine)

канд. мед. наук

MD, Cand. Sci. (Medicine)

VasilevYA1@zdrav.mos.ruResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesНаучно-практический клинический центр диагностики и телемедицинских технологийResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesLomonosov Moscow State UniversityМосковский государственный университет имени М.В. ЛомоносоваLomonosov Moscow State UniversityEndocrinology Research CentreНациональный медицинский исследовательский центр эндокринологииEndocrinology Research CentreThe First Sechenov Moscow State Medical UniversityПервый Московский государственный медицинский университет имени И.М. СеченоваThe First Sechenov Moscow State Medical University1810202404122024535055180612202323082024Copyright ©; 2024, Eco-VectorCopyright ©; 2024, Эко-векторCopyright ©; 2024, Eco-Vector2024Eco-VectorЭко-векторEco-Vectorhttps://creativecommons.org/licenses/by-nc-nd/4.0https://jdigitaldiagnostics.com/DD/article/view/624250

BACKGROUND: Osteoporosis is often diagnosed at the stage with complications, i.e., low-energy fractures. Vertebral compression fractures, which are complications of osteoporosis and predictors of subsequent fractures, are often asymptomatic. Compression fractures can be found by computed tomography performed for other indications with vertebral morphometry. Approaches to using artificial intelligence algorithms designed for diagnosing vertebral compression fractures were analyzed.

AIM: Testing artificial intelligence algorithms to conduct morphometric analysis of vertebrae on chest computed tomography scans and assess the possibility of their implementation in medical organizations of the Moscow Healthcare Department.

MATERIALS AND METHODS: To set a clinical task for artificial intelligence algorithms, basic diagnostic requirements in the area of “vertebral compression fractures (osteoporosis)” were formulated. The testing of the artificial intelligence algorithms included the following stages: self-testing, functional and calibration testing, practical evaluation, and operation testing. The first three stages of testing were performed using previously generated datasets. At practical evaluation and operation testing, artificial intelligence algorithms analyzed the data from computed tomography performed in medical organizations. The expert group of radiologists assessed the diagnostic accuracy and functional capacity of the AI algorithms at all stages. The resulting quantitative metrics of the accuracy of artificial intelligence algorithms were compared with the required target values.

RESULTS: From June 2021 to June 2022, two artificial intelligence algorithms (Nos. 1 and 2) with different methods of detecting compression fractures were tested. Both artificial intelligence algorithms successfully passed the self-testing (6 tests), functional (5 tests), and calibration (100 tests) stages. The area under the ROC curve for artificial intelligence algorithm No. 1 was 0.99 (95% CI, 0.98–1), and for artificial intelligence algorithm No. 2, it was 0.91 (95% CI, 0.85–0.96). Artificial intelligence algorithm No. 1 passed the practical evaluation stage without any significant remarks, whereas algorithm No. 2 was sent for fine-tuning. After the operation testing stage, the following accuracy metrics were obtained: the areas under the ROC curve for artificial intelligence algorithm Nos. 1 and 2 were 0.93 (95% CI, 0.89–0.96) and 0.92 (95% CI, 0.90–0.94), respectively. At all stages, both artificial intelligence algorithms demonstrated sufficient metrics for clinical validation.

CONCLUSION: Artificial intelligence algorithms for the automatic diagnosis of vertebral compression fractures have been tested, demonstrating the high quality of their operation. artificial intelligence algorithms can be applied as a supplementary tool in the medical decision support system.

Обоснование. Остеопороз зачастую диагностируется на этапе возникновения осложнений — низкоэнерегетических переломов. Компрессионные переломы тел позвонков — осложнение остеопороза и одновременно предиктор последующих переломов различной локализации — нередко протекают бессимптомно. Компрессионные переломы возможно выявить при исследованиях методом компьютерной томографии, выполненных по другим показаниям, путём проведения морфометрии тел позвонков. Нами проанализированы подходы использования сервисов искусственного интеллекта, предназначенных для диагностики компрессионных переломов тел позвонков.

Цель — тестирование сервисов искусственного интеллекта для проведения морфометрического анализа тел позвонков по данным компьютерной томографии органов грудной клетки, а также оценка возможности их внедрения в практику медицинских организаций Департамента здравоохранения города Москвы.

Материалы и методы. Для постановки клинической задачи сервисам искусственного интеллекта были сформированы базовые диагностические требования по направлению «Компрессионный перелом тел позвонков (остеопороз)». Сервисы проходили следующие этапы: самотестирование, функциональное и калибровочное тестирование, апробацию и опытную эксплуатацию. Для первых трёх этапов тестирование проводилось на ранее подготовленных наборах данных. На этапе апробации и опытной эксплуатации с помощью сервисов искусственного интеллекта анализировали данные исследований методом компьютерной томографии, выполненных в медицинских организациях. На всех этапах работала экспертная группа врачей, оценивая диагностическую точность и функциональную полноценность сервисов. Сравнивались полученные количественные метрики точности работы сервисов искусственного интеллекта с целевыми значениями.

Результаты. В период с июня 2021 по июнь 2022 года проходили тестирование два сервиса искусственного интеллекта (№ 1 и № 2), которые используют отличные друг от друга методы определения наличия компрессионных переломов. Оба сервиса успешно прошли этап самотестирования (6 исследований), а также функциональное (5 исследований) и калибровочное (100 исследований) тестирование. Площадь под ROC-кривой составила 0,99 (здесь и далее в скобках указаны значения 95% доверительного интервала; 0,98–1) для сервиса № 1, и 0,91 (0,85–0,96) для сервиса № 2. Этап апробации сервис № 1 прошёл без существенных замечаний, в то время как сервис № 2 был отправлен на доработку. После этапа опытной эксплуатации были получены следующие метрики точности: для сервиса № 1 площадь под ROC-кривой составила 0,93 (0,89–0,96), для сервиса № 2 — 0,92 (0,90–0,94). На всех этапах выбранные сервисы искусственного интеллекта показали метрики достаточные для клинической валидации.

Заключение. Проведено тестирование сервисов искусственного интеллекта, выполняющих автоматизированную диагностику компрессионных переломов тел позвонков. Продемонстрировано высокое качество их работы. Сервисы на основе искусственного интеллекта могут быть использованы в качестве вспомогательного инструмента в системе поддержки принятия врачебных решений.

论证。骨质疏松症通常在出现并发症阶段（低能量骨折）时才被诊断出来。 椎体压缩性骨折是骨质疏松症的一种并发症，同时也是随后不同部位骨折的预测因素，但通常没有症状。在针对其他适应症而进行的计算机断层扫描，借助椎体形态测量可以检测出压缩性骨折。 我们分析了使用人工智能服务诊断椎体压缩性骨折的方法。

目的 — 测试根据胸部计算机断层扫描数据对椎体进行形态测量分析的人工智能服务，并评估其在莫斯科市卫生局医疗机构实践中推广的可能性。

材料和方法。为了设定人工智能服务的临床任务，形成了“椎体压缩性骨折（骨质疏松）”为方向的基本诊断要求。服务通过了以下阶段：自测、功能测试、校准测试、验证和试运行。在前三个阶段，测试是在先前准备好的数据集上进行的。 在验证和试运行阶段，使用人工智能服务对医疗机构的计算机断层扫描的检测数据进行了分析。在各个阶段医生专家小组开展工作，评估服务的诊断准确性和功能实用性。将获得的人工智能服务准确性的定量指标与目标值进行比较。

结果。2021年6月至2022年6月期间测试了两种人工智能服务（№1和№2），它们使用不同的方法来确定是否存在压缩性骨折。 两项服务都成功通过了自测阶段（6次试验），以及功能测试（5次试验）和校准测试（100次试验）。№1服务的ROC曲线下面积为0.99（括号内为 95%置信区间值；0.98-1），№2服务的ROC曲线下面积为 0.91（0.85-0.96）。№1服务通过了验证阶段，没有重大意见，而№2服务则被送去修改。试运行阶段结束后，准确度指标如下：№1服务的ROC曲线下面积为 0.93（0.89-0.96），№2服务的ROC曲线下面积为 0.92（0.90-0.94）。在所有阶段，选定的人工智能服务都显示出足以进行临床验证的指标。

结论。对自动诊断椎体压缩性骨折的人工智能服务进行了测试。人工智能服务表现出很高的工作质量。 基于人工智能的服务可以作为医疗决策支持系统的辅助工具。

osteoporosiscomputed tomographycompression fractureartificial intelligenceостеопорозкомпьютерная томографиякомпрессионный переломискусственный интеллект骨质疏松症计算机断层扫描压缩性骨折人工智能Департамент здравоохранения города МосквыMoscow Health Care DepartmentMoscow Health Care Department123031400007-7This article was prepared by the authors as part of the research and development work (EGISU number: 123031400007-7) in accordance with the Program of the Moscow Department of Health for 2023-2025Данная статья подготовлена в рамках НИОКР «Разработка и создание аппаратно-программного комплекса для оппортунистического скрининга остеопороза» (No ЕГИСУ:123031400007-7)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/osteo12930Белая Ж.Е., Белова К.Ю., Бирюкова Е.В., и др. 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