Digital DiagnosticsDigital DiagnosticsDigital DiagnosticsDigital Diagnostics2712-84902712-8962Eco-Vector32196310.17816/DD321963Original Study ArticlesОригинальные исследования原创性科研成果Research ArticleAnalysis of the diagnostic and economic impact of the combined artificial intelligence algorithm for analysis of 10 pathological findings on chest computed tomographyДиагностическая и экономическая оценка применения комплексного алгоритма искусственного интеллекта, направленного на выявление десяти патологических находок по данным компьютерной томографии органов грудной клетки旨在从胸部电子计算机断层扫描中识别十种病理检查所见的综合人工智能算法使用的诊断和经济评估https://orcid.org/0000-0002-0302-293X57210638679AAF-1215-20208896-8051CherninaValeria Yu.ЧернинаВалерия ЮрьевнаCherninaValeria Yu.
Russian Federation
v.chernina@ira-labs.comhttps://orcid.org/0000-0001-9906-64532406-1772BelyaevMikhail G.БеляевМихаил ГеннадьевичBelyaevMikhail G.
Russian Federation

Cand. Sci. (Phys.-Math.), Professor

канд. физ.-мат. наук, профессор

Cand. Sci. (Phys.-Math.), Professor

belyaevmichel@gmail.comhttps://orcid.org/0000-0003-4952-23474411-8745SilinAnton Yu.СилинАнтон ЮрьевичSilinAnton Yu.
Russian Federation
silin@yamed.ruhttps://orcid.org/0009-0007-3550-7556AvetisovIvan O.АветисовИван ОлеговичAvetisovIvan O.
Russian Federation
avetisov@yamed.ruhttps://orcid.org/0000-0002-2827-14736150-4961PyatnitskiyIlya A.ПятницкийИлья АркадьевичPyatnitskiyIlya A.
Russian Federation
i.pyatnitskiy@ira-labs.comhttps://orcid.org/0000-0001-6572-53696910-8890PetrashEkaterina A.ПетрашЕкатерина АлександровнаPetrashEkaterina A.
Russian Federation

MD, Cand. Sci. (Med.)

канд. мед. наук

MD, Cand. Sci. (Med.)

e.a.petrash@gmail.comhttps://orcid.org/0009-0000-3325-8452BasovaMaria V.БасоваМария ВасильевнаBasovaMaria V.
Russian Federation
m.basova@ira-labs.comhttps://orcid.org/0000-0002-5649-21938449-6590SinitsynValentin E.СиницынВалентин ЕвгеньевичSinitsynValentin E.
Russian Federation

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

д-р мед. наук, профессор

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

vsini@mail.ruhttps://orcid.org/0000-0003-1581-07031776-4270OmelyanovskiyVitaly V.ОмельяновскийВиталий ВладимировичOmelyanovskiyVitaly V.
Russian Federation

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

д-р мед. наук, профессор

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

vvo@rosmedex.ruhttps://orcid.org/0000-0003-1816-13156810-3279GombolevskiyVictor A.ГомболевскийВиктор АлександровичGombolevskiyVictor A.
Russian Federation

MD, Cand. Sci. (Med.)

канд. мед. наук

MD, Cand. Sci. (Med.)

gombolevskii@gmail.comIRA LabsАЙРА ЛабсIRA LabsClinical Hospital on YauzaКлинический госпиталь на ЯузеClinical Hospital on YauzaThe University of Texas at AustinТехасский университет в ОстинеThe University of Texas at AustinN.N. Blokhin National Medical Research Center of OncologyНациональный медицинский исследовательский центр онкологии имени Н.Н. БлохинаN.N. Blokhin National Medical Research Center of OncologyLomonosov Moscow State UniversityМосковский государственный университет имени М.В. ЛомоносоваLomonosov Moscow State UniversityResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesНаучно-практический клинический центр диагностики и телемедицинских технологийResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesThe Center for Healthcare Quality Assessment and ControlЦентр экспертизы и контроля качества медицинской помощиThe Center for Healthcare Quality Assessment and ControlRussian Medical Academy of Continuous Professional EducationРоссийская медицинская академия непрерывного профессионального образованияRussian Medical Academy of Continuous Professional EducationScientific and research financial instituteНаучно-исследовательский финансовый институтScientific and research financial instituteArtificial Intelligence Research InstituteИнститут искусственного интеллектаArtificial Intelligence Research Institute0106202312072023421051320704202322052023Copyright ©; 2023, Eco-VectorCopyright ©; 2023, Эко-векторCopyright ©; 2023, Eco-Vector2023Eco-VectorЭко-векторEco-Vectorhttps://creativecommons.org/licenses/by-nc-nd/4.0https://jdigitaldiagnostics.com/DD/article/view/321963

BACKGROUND: Artificial intelligence technology can help solve the significant problem of missed findings in radiology studies. An important issue is assessing the economic benefits of implementing artificial intelligence.

AIM: To evaluate the frequency of missed pathologies detection and the economic potential of artificial intelligence technology for chest computed tomography compared and validated by experienced radiologists.

MATERIALS AND METHODS: This was an observational, single-center retrospective study. The study included chest computed tomography without IV contrast from June 1 to July 31, 2022, in Clinical Hospital in Yauza, Moscow. The computed tomography was processed using a complex artificial intelligence algorithm for 10 pathologies: pulmonary infiltrates, typical for viral pneumonia (COVID-19 in pandemic conditions); lung nodules; pleural effusion; pulmonary emphysema; thoracic aortic dilatation; pulmonary trunk dilatation; coronary artery calcification; adrenal hyperplasia; and osteoporosis (vertebral body height and density changes). Two experts analyzed computed tomography and compared results with artificial intelligence. Further routing was determined according to clinical guidelines for all findings initially detected and missed by radiologists. The hospital price list determined the potential revenue loss for each patient.

RESULTS: From the final 160 computed tomographies, the artificial intelligence identified 90 studies (56%) with pathologies, of which 81 (51%) were missing at least one pathology in the report. The “second-stage” lost potential revenue for all pathologies from 81 patients was RUB 2,847,760 ($37,251 or CNY 256,218). Lost potential revenue only for those pathologies missed by radiologists but detected by artificial intelligence was RUB 2,065,360 ($27,017 or CNY 185,824).

CONCLUSION: Using artificial intelligence as an “assistant” to the radiologist for chest computed tomography can dramatically minimize the number of missed abnormalities. Compared with the normal model without artificial intelligence, using artificial intelligence can provide 3.6 times more benefits. Using advanced artificial intelligence for chest computed tomography can save money.

Обоснование. Технологии искусственного интеллекта призваны помогать в решении проблемы пропуска находок при лучевых исследованиях. Важным вопросом является оценка экономической пользы от внедрения технологий искусственного интеллекта.

Цель ― оценить частоту выявления патологических находок и экономический потенциал применения комплексного искусственного интеллекта для компьютерной томографии органов грудной клетки, валидированного экспертами, по сравнению с рентгенологами без доступа к технологиям в условиях частного медицинского центра.

Материалы и методы. Проведено обсервационное одноцентровое ретроспективное исследование. В исследование включались компьютерные томограммы органов грудной клетки без внутривенного контрастирования, выполненные в ООО «Клинический госпиталь на Яузе» (Москва) в период с 01.06.2022 по 31.07.2022. Компьютерные томограммы обработаны комплексным алгоритмом искусственного интеллекта для десяти патологий: инфильтративные изменения в лёгких, характерные для вирусной пневмонии (COVID-19 в условиях пандемии); лёгочные узлы; свободная жидкость в плевральных полостях; эмфизема лёгких; увеличение диаметра грудной аорты; увеличение диаметра ствола лёгочной артерии; коронарный кальциноз; оценка толщины надпочечников; оценка высоты и плотности тел позвонков. Два эксперта анализировали компьютерные томограммы и сравнивали результаты с анализом искусственного интеллекта. Для всех находок, выявленных и не выявленных врачами клиники, определили дальнейшую маршрутизацию в соответствии с клиническими рекомендациями. Для каждого пациента была рассчитана стоимость неоказанных медицинских услуг по прайс-листу клиники.

Результаты. Итоговую группу составили 160 компьютерных томограмм органов грудной клетки с описаниями. С помощью искусственного интеллекта выявлено 90 (56%) исследований с патологиями, из них в 81 (51%) протоколе была пропущена хотя бы одна патология. Общая стоимость неоказанных медицинских услуг «второго этапа» для всех патологий от 81 пациента была оценена в 2 847 760 руб. (37 250,99 долларов или 256 217,95 китайских юаней). Стоимость неоказанных медицинских услуг только для тех патологий, которые пропущены врачами, но выявлены искусственным интеллектом, составила 2 065 360 руб. (27 016,57 долларов или 185 824,05 китайских юаней).

Заключение. Применение искусственного интеллекта для анализа данных компьютерной томографии органов грудной клетки в качестве помощника рентгенолога позволяет существенно уменьшить число случаев пропуска патологий. Использование искусственного интеллекта может принести в 3,6 раза больше стоимости за медицинские услуги по сравнению со стандартной моделью работы рентгенологов без применения таких технологий, и, таким образом, быть рентабельным для применения в условиях частного медицинского центра.

论证。人工智能技术打算帮助解决射线检验中遗漏发现的问题。一个重要的问题是对采用人工智能技术的经济效益进行的评估。

该研究的目的是评估在私人医疗中心环境下，与不应用技术的放射科医生相比，使用全面的、经过专家验证的人工智能进行胸部电子计算机断层扫描的检测频率和经济潜力。

材料和方法。进行了一项观察性、单中心的回顾性研究。本研究包括2022年6月1日至2022年7月31日在“Clinical Hospital on Yauza”（莫斯科）进行的没有静脉注射对比剂的胸部器官电子计算机断层扫描图像。电子计算机断层扫描图像由人工智能的综合算法处理，用于10种病症：病毒性肺炎（大流行条件下的COVID-19）的肺部浸润性病变；肺结节；胸膜腔内的游离液体；肺气肿；胸主动脉增宽；肺动脉干增宽；冠状动脉钙化；肾上腺厚度的评估；椎体高度和密度的评估。两位专家分析了电子计算机断层扫描图像，并对结果与人工智能分析进行了比较。对于诊所医生检测到和未检测到的所有发现，根据临床指南确定了进一步路由。对于每个病人，根据诊所的价格表，计算出未提供的医疗服务费用。

结果。最后一组由160个带有描述的胸部器官电子计算机断层扫描图像组成。人工智能识别出90个（56%）有病变的研究，其中81个（51%）协议至少有一个遗漏的病变。81名患者的所有病变的未提供的“第二阶段”医疗服务的总成本估计为2,847,760卢布（37,250.99美元或256,217.95人民币）。只有那些被医生遗漏但被人工智能检测出来的病变的未提供医疗服务费用为2,065,360卢布（27,016.57美元或185,824.05人民币）。

结论。来为分析胸部电子计算机断层扫描数据而使用的作为放射科医生助手的人工智能允许大大减少遗漏病变的情况。与不应用这种技术放射科医生工作的标准模式相比，使用人工智能可以为每项医疗服务带来3.6倍的成本，因此，在私人医疗中心环境下的应用具有成本效益。

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