Page 87 - 中国全科医学2022-11

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Corresponding author：ZHANG Haicheng，Chief physician；E-mail：haichengzhang@bjmu.edu.cn
【Abstract】 Background The cloud-based platform for electrocardiography（ECG）analysis plays a supporting role
in the prevention and treatment of cardiovascular diseases. During the construction of a cloud-based platform for ECG analysis，
problems that should be focused and addressed are exploring ways to better use artificial intelligence（AI） technologies
supporting ECG analysis，and improving the process and effectiveness of AI-aided diagnosis of a critical ECG. Objective
To explore the use of AI technologies in a cloud-based platform for ECG analysis to support the diagnosis of a critical ECG in
primary care. Methods The 12-lead resting ECGs（n=20 808） uploaded to Nalong Cloud-based ECG Analysis Platform by
primary healthcare institutions were selected from June 2019 to June 2021. After being interpreted by AI-based algorithms and
physicians，respectively，ECG findings were classified into critical group（critical ECGs），normal group（normal ECGs），
and positive group（abnormal but not critical ECGs）. The results interpreted by the AI-based algorithm were compared with
those interpreted by physicians（defined as the gold standard） to assess the diagnostic agreement and coincidence rate between
AI-based and physician-based interpretations，and to assess the diagnostic sensitivity，and positive predictive value of AI-
based interpretation. And the mean time for making diagnoses of three groups of ECGs was calculated. Results By the AI-based
interpretation，619，15 634 and 45 55 ECGs were included in the critical，positive，and normal groups，respectively. And by
the physician-based interpretation，619，15 759 and 4 430 ECGs were included in the critical，positive，and normal groups，
respectively. There was high agreement between AI-based and physician-based interpretation results of ECGs〔Kappa=0.984，
95%CI（0.982，0.987），P<0.001〕，with a diagnostic coincidence rate of 99.4%. The diagnostic sensitivity and positive
predictive value of AI-based interpretation for ECGs was 99.4%，and 100.0%，respectively. The mean time for making diagnoses
of critical ECGs，abnormal but not critical ECGs，and normal ECGs was statistically different （P<0.001），the mean time
of critical critical ECGs was shorter than normal ECGs and abnormal but not critical ECGs（P<0.001）. Conclusion AI
technologies used in a cloud-based platform for ECG analysis could provide physicians with support for interpreting ECGs，which
may contribute to improving the interpretation accuracy，optimizing the diagnostic process，shortening the time for diagnosing a
critical ECG，and the treating of critical patients in primary care.
【Key words】 Cardiovascular Diseases；Artificial intelligence；Diagnostic techniques，cardiovascular；
Electrocardiography；Remote electrocardiography cloud platform；Primary medical institutions；Diagnosis

心电图诊断现广泛应用于临床，其针对心血管疾病 本研究价值：
的检查尤为有效，但我国精通心电图诊断的医生比例不 （1）本研究对人工智能（AI）在远程心电云平
到 3％，供需比严重不平衡（1 ∶ 6 944） ［1-2］ ，诊疗人 台中的应用加以阐述，有利于相关工作者了解 AI 在
才分布不均衡，基层医疗卫生机构（如社区卫生服务站、 远程心电云平台建设中的应用价值；（2）本研究较
社区卫生服务中心、乡镇卫生院等）缺乏精通心电图诊 详尽地描述了如何通过 AI 技术提升“危急值”心电
断的医生（该类医生多任职于大型三甲医院） ［3］ 。随 图诊断时效性，为今后 AI 在远程心电云平台的应用
着计算机技术、通信技术与医疗技术的发展，各地纷纷 指明了研究方向。
通过建设“远程心电云平台”的方法建立“基层检查、 本研究局限性：
上级诊断”的模式以解决上述矛盾。但是，随着我国人
本研究仅列举了 AI 在远程心电云平台辅助决策
口老龄化进程的加快、慢性病人群数量逐年增长，基层
基层危急值心电图中的应用，而未涉及 AI 技术辅助
医疗卫生机构采集并上传至远程心电诊断中心的心电数
远程心电云平台上其他不同级别与性质的医疗机构，
据呈几何级增长，危急值心电图的比例也相应增加。如
及针对同一医疗机构中不同科室的心电数据进行快速
何辅助医生更好更快地完成心电图诊断、解决基层医疗
准确诊断的应用。
卫生机构心电图诊断水平参差不齐的问题、满足社会日
益增长的心电图诊断需求，已成为临床亟待解决的重要 已经成为医疗创新的前沿领域，从新药研发、疾病预测、
问题。 高级成像到医疗管理等环节，均离不开 AI 技术的支持，
人工智能（artificial intelligence，AI）起源于 1950 AI 已成为医疗行业的有力辅助和支撑 ［6-7］ 。因此，探
年艾伦·图灵对机器智能的测试。AI 通过利用各种模 索如何应用 AI 技术更好地协同医生判读心电图、优化
糊逻辑理论的分类及回归算法、人工神经网络算法、机 心电图诊断流程、提高危急值心电图诊断时效性显得尤
器学习算法等技术手段，根据输入的经验和信息，以及 为重要。本研究通过分析基层医疗卫生机构采集并上传
构建概念，完成人类易于执行但难于形式化描述的任务， 至远程心电云平台的 20 808 份 12 导联静态心电图，阐
其信息存储和信息处理能力优势明显 ［4-5］ 。目前，AI 述 AI 在远程心电云平台辅助决策基层危急值心电图中