| 于娜,谭景斐,江潭耀,程山珊,瞿晶,占斯慧,郑孝意,潘敏仪,吕媛浩,张忠平.人工智能与康复医师评估脑卒中住院患者ICF-RS功能分级的一致性研究[J].中国康复,2025,40(3):140-144 |
| 人工智能与康复医师评估脑卒中住院患者ICF-RS功能分级的一致性研究 |
| Consistency study on stroke inpatients’ ICF functional grading between artificial intelligence and rehabilitation physician |
| |
| DOI：10.3870/zgkf.2025.03.003 |
| 中文关键词: 国际功能、残疾和健康分类康复组合 康复医师 人工智能 一致性 |
| 英文关键词: ICF Rehabilitation Set Rehabilitation physician Artificial intelligence Consistency analysis |
| 基金项目:番禺区科技计划一般医疗卫生项目（2020-Z04-080） |
|
| 摘要点击次数: 413 |
| 全文下载次数: 336 |
| 中文摘要: |
| 目的：检验人工智能神经网络算法模型与康复医师评估脑卒中住院患者ICF-RS功能分级的一致性。方法：先由同一病区的康复医师接受国标ICF-RS培训，共同评估3例脑卒中住院患者，熟悉评估流程，掌握评判标准。随后，每位康复医师在日常诊疗工作中常规采用ICF-RS评估106例住院脑卒中患者，并对身体、活动、参与三个维度及整体功能进行分级，同时与经人工智能神经网络算法模型构建的功能等级比较。数据收集后，以Kendall和谐系数评价康复医师共同评定3例患者各类目限定值的一致性，加权Kappa系数和组内相关系数（ICC）评价人工智能算法模型与康复医师临床评估功能分级之间的一致性。结果：康复医师评定3位患者ICF-RS 类目的Kendall 和谐系数分别为0.765、0.849、0.874（均P<0. 01），随评估患者增多一致性提高；人工智能算法模型与康复医师对106例住院脑卒中患者在身体功能、活动、参与三个维度及整体功能分级的加权Kappa系数分别为0.718、0.737、0.750、0.825（均P<0. 01），ICC系数分别为0.789、0.801、0.806、0.862（均P<0. 01）。结论：由康复医师采用国标ICF-RS评定住院脑卒中患者的一致性好，人工智能算法模型与医师临床评定的一致性高。 |
| 英文摘要: |
| Objective: To test the consistency of artificial intelligence neural network algorithm model and rehabilitation physicians’ assessment of ICF-RS function classification of stroke inpatients. Methods: After national standardized ICF-RS training, rehabilitation physicians in the same ward evaluated 3 stroke inpatients. They were familiar with the evaluation process and mastered the evaluation criteria. Subsequently, each rehabilitation physician routinely used ICF-RS to evaluate 106 hospitalized stroke patients in their daily diagnosis and treatment work, and graded the three dimensions of body, activity and participation and the overall function, and compared with the functional level constructed by artificial intelligence neural network algorithm model. After data collection, Kendall’s coefficient of concordance was used to evaluate the consistency of the limited values of various items in three patients, and weighted kappa coefficient and intra group correlation coefficient (ICC) were used to evaluate the consistency between the prediction of artificial intelligence algorithm model and the functional classification of rehabilitation physicians’ clinical evaluation. Results: Kendall’s coefficient of concordance of ICF-RS categories of the three patients were 0.765, 0.849, and 0.874 respectively (all P<0.01), and the consistency was improved with the increase of patients. The weighted Kappa for the artificial intelligence algorithm model and rehabilitation physicians in determining functional classification in physical function, activity, participation, and overall functional grading of 106 inpatients with stroke were 0.718, 0.737, 0.750, and 0.825 respectively (all P<0.01), and the ICC coefficients were 0.789, 0.801, 0.806, and 0.862 respectively (all P<0.01). Conclusion: The consistency of ICF-RS assessment of hospitalized stroke patients by rehabilitation physicians is good, and the consistency of artificial intelligence algorithm model and clinical assessment of physicians is high. |
|
查看全文
下载PDF阅读器 HTML全文 |
| 关闭 |
|
|
|
