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

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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。

作者简介

Valeria Yu. Chernina

IRA Labs

Email: v.chernina@ira-labs.com
ORCID iD: 0000-0002-0302-293X
SPIN 代码: 8896-8051
Scopus 作者 ID: 57210638679
Researcher ID: AAF-1215-2020
俄罗斯联邦, Moscow

Mikhail G. Belyaev

IRA Labs

Email: belyaevmichel@gmail.com
ORCID iD: 0000-0001-9906-6453
SPIN 代码: 2406-1772

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

俄罗斯联邦, Moscow

Anton Yu. Silin

Clinical Hospital on Yauza

Email: silin@yamed.ru
ORCID iD: 0000-0003-4952-2347
SPIN 代码: 4411-8745
俄罗斯联邦, Moscow

Ivan O. Avetisov

Clinical Hospital on Yauza

Email: avetisov@yamed.ru
ORCID iD: 0009-0007-3550-7556
俄罗斯联邦, Moscow

Ilya A. Pyatnitskiy

IRA Labs; The University of Texas at Austin

Email: i.pyatnitskiy@ira-labs.com
ORCID iD: 0000-0002-2827-1473
SPIN 代码: 6150-4961
俄罗斯联邦, Moscow; Austin, Texas, USA

Ekaterina A. Petrash

IRA Labs; N.N. Blokhin National Medical Research Center of Oncology

Email: e.a.petrash@gmail.com
ORCID iD: 0000-0001-6572-5369
SPIN 代码: 6910-8890

MD, Cand. Sci. (Med.)

俄罗斯联邦, Moscow; Moscow

Maria V. Basova

IRA Labs

Email: m.basova@ira-labs.com
ORCID iD: 0009-0000-3325-8452
俄罗斯联邦, Moscow

Valentin E. Sinitsyn

Lomonosov Moscow State University; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: vsini@mail.ru
ORCID iD: 0000-0002-5649-2193
SPIN 代码: 8449-6590

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

俄罗斯联邦, Moscow; Moscow

Vitaly V. Omelyanovskiy

The Center for Healthcare Quality Assessment and Control; Russian Medical Academy of Continuous Professional Education; Scientific and research financial institute

Email: vvo@rosmedex.ru
ORCID iD: 0000-0003-1581-0703
SPIN 代码: 1776-4270

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

俄罗斯联邦, Moscow; Moscow; Moscow

Victor A. Gombolevskiy

IRA Labs; Artificial Intelligence Research Institute

编辑信件的主要联系方式.
Email: gombolevskii@gmail.com
ORCID iD: 0000-0003-1816-1315
SPIN 代码: 6810-3279

MD, Cand. Sci. (Med.)

俄罗斯联邦, Moscow; Moscow

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2. Fig. 1. Study design.

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3. Fig. 2. Study result by the number of findings detected with and without AI.

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4. Fig. 3. Number of findings (ranked by the number of significant missed pathological findings).

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5. Fig. 4. Analysis of the cost of medical services not provided because of missed pathological findings, and all CT scans performed.

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6. Fig. 5. Range of costs of medical services not provided due to the use of the combined AI service for chest CT scans in a clinic. AI, artificial intelligence; CT, computed tomography; CNMS, cost of non-provided medical services.

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7. Fig. 6. An example of AI use. Patient B, 76 years old. A radiologist correctly identified bilateral hydrothorax and emphysematous changes but did not describe the lung nodule in the right lung. An AI algorithm revealed all three pathological findings: hydrothorax is highlighted with a yellow line, emphysematous changes are highlighted in orange, and the lung nodule is indicated by a red square.

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8. Fig. 7. An example of AI use. Patient B., 79 years old. Chest CT scans: a) axial section: a radiologist and an algorithm correctly identified a lung nodule in the left lung (indicated by a red square) and coronary calcification (outlined by an orange line). In addition, the algorithm indicated an increase in the volume of epicardial fat (filled in yellow; this pathological finding was not considered in the study); b) sagittal section: a radiologist and an algorithm correctly identified compression fractures of Th6 and Th9 vertebral bodies, Genant 3 (three columns are marked with red lines); however, the radiologist did not indicate deformities of Th5 and Th12 vertebral bodies, Genant 2 (three columns are marked with yellow lines) in the protocol.

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9. Fig. 8. Potential cost of medical services not provided because a combined AI service was not used for chest CT scans in a clinic, considering the cost of using AI.

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10. 图1。研究设计。

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11. 图2。在使用/不使用人工智能的情况下所见的发现数量的研究结果。

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12. 图3。发现数量的研究结果（按重大遗漏数量排列）。

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13. 图4。在进行电子计算机断层扫描（CT）的范围内，由于遗漏病变而导致的未提供医疗服务的费用分析。

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14. 图5。医疗机构使用复合人工智能进行胸部CT检查的未提供医疗服务的费用谱。CT——电子计算机断层扫描。

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15. 图6。人工智能算法工作原理的实例。患者B，76岁。医生正确地发现了双侧胸水和气肿性改变，但没有描述右肺的肺结节。人工智能算法确定了所有3种病变：胸水用黄线勾勒，气肿性改变用橙色标出，肺结节用红色方块标记。

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16. 图7。人工智能算法工作原理的实例。患者V，79岁。胸部计算机断层扫描：a——轴向切片：医生和算法正确地发现了左肺的肺结节（用红色方块标记）和冠状动脉钙化（用橙线勾勒）；此外，算法显示了心外膜脂肪体积增加（用黄色填充，该病变在研究中没有考虑）；b——矢状切片：医生和算法正确地发现了Th6和Th9椎体的压缩性骨折，Genant 3（3列用红线标记），但医生没有在协议书中指出Th5和Th12椎体的畸形，Genant 2（3列用黄线标记）。

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17. 图8。考虑到使用人工智能的成本，医疗机构由于没有使用复合人工智能进行胸部CT检查而导致的未提供医疗服务的费用可行性。

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