摘要
样本量的准确评估是评价诊断试验结果是否可靠的重要因素之一。本文以人工智能识别肝脏超声造影影像学数据的临床诊断试验为例,进行二分类和多分类研究,以敏感性和特异性为主要指标,结合疾病的发生率、检验水准、单双侧检验等统计特征进行样本量估算。综合人工智能医学图像识别中训练集/测试集数据比例划分、病例脱落率等因素对计算样本量进行校正。Sample Size Calculator、MedCalc、PASS等软件的应用可实现样本量的快速计算,减少人为工作量。
Sample size calculation is an important factor to evaluate the reliability of the diagnostic test. In this paper, a case study of the clinical diagnostic test of artificial intelligence for identification of liver contrast-enhanced ultrasound was performed to conduct two-category and multi-categories studies. Based on sensitivity and specificity, the sample size was then estimated in combination with the statistical characteristics of disease incidence, test level and one/two-sided test. Eventually, the sample size was corrected by integrating the factors of the proportion of training/test dataset and the dropout rate of cases in the medical image recognition system. Moreover, the application of Sample Size Calculator, MedCalc, PASS, and other software can accelerate sample size calculation and reduce the amount of labor.
作者
刘灯
刘丽
曾杨媚
郭燕丽
LIU Deng;LIU Li;ZENG Yangmei;GUO Yanli(Department of Ultrasound,the First Hospital Affiliated to Army Medical University,Chongqing 400038,P.R.China)
出处
《中国循证医学杂志》
CSCD
北大核心
2021年第3期361-366,共6页
Chinese Journal of Evidence-based Medicine
基金
国家国际科技部科技合作专项项目(编号:2015DFA30920)
陆军军医大学第一附属医院重大领域技术创新项目(编号:SWH2016ZDCX4101)。
关键词
样本量估计
敏感性
特异性
人工智能
超声造影
Sample size calculation
Sensitivity
Specificity
Artificial intelligence
Contrast-enhanced ultrasound
