The sample size calculation for artificial intelligence diagnosis of contrast-enhanced ultrasound based on sensitivity and specificity_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Deng , LIU Li , ZENG Yangmei ,  GUO Yanli

Department of Ultrasound, the First Hospital Affiliated to Army Medical University, Chongqing 400038, P.R.China;

Corresponding author：

GUO Yanli, Email: guoyanli71@aliyun.com

Keywords：

Sample size calculation; Sensitivity; Specificity; Artificial intelligence; Contrast-enhanced ultrasound

DOI：

10.7507/1672-2531.202009050

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： LIU Deng, LIU Li, ZENG Yangmei, GUO Yanli. The sample size calculation for artificial intelligence diagnosis of contrast-enhanced ultrasound based on sensitivity and specificity. Chinese Journal of Evidence-Based Medicine, 2021, 21(3): 361-366. doi: 10.7507/1672-2531.202009050 Copy

1.	Tang C, Fang K, Guo Y, et al. Safety of sulfur hexafluoride microbubbles in sonography of abdominal and superficial organs: retrospective analysis of 30, 222 cases. J Ultrasound Med, 2017, 36(3): 531-538.
2.	Zhou LQ, Wang JY, Yu SY, et al. Artificial intelligence in medical imaging of the liver. World J Gastroenterol, 2019, 25(6): 672-682.
3.	Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer, 2018, 18(8): 500-510.
4.	朱一丹, 李会娟, 武阳丰. 诊断准确性研究报告规范(STARD)2015介绍与解读. 中国循证医学杂志, 2016, 16(6): 730-735.
5.	Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Clin Chem, 2015, 61(12): 1446-1452.
6.	刘丹, 周吉银. 临床科研项目样本量的要求. 中国医学伦理学, 2019, 32(6): 716-718, 723.
7.	Buderer NM. Statistical methodology: I. Incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. Acad Emerg Med, 1996, 3(9): 895-900.
8.	Baratloo A, Hosseini M, Negida A, et al. Part 1: simple definition and calculation of accuracy, sensitivity and specificity. Emerg (Tehran), 2015, 3(2): 48-49.
9.	Negida A, Fahim NK, Negida Y. Sample size calculation guide - part 4: how to calculate the sample size for a diagnostic test accuracy study based on sensitivity, specificity, and the area under the ROC curve. Adv J Emerg Med, 2019, 3(3): e33.
10.	Wu K, Chen X, Ding M. Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Intern J Light Elect Opt, 2014, 125(15): 4057-4063.
11.	Guo LH, Wang D, Qian YY, et al. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clin Hemorheol Microcirc, 2018, 69(3): 343-354.
12.	Malhotra RK, Indrayan A. A simple nomogram for sample size for estimating sensitivity and specificity of medical tests. Indian J Ophthalmol, 2010, 58(6): 519-522.
13.	倪延延, 张晋昕. 假设检验时样本含量估计中容许误差δ的合理选取. 循证医学, 2011, 11(6): 370-372.
14.	Hassan TM, Elmogy M, Sallam E. Diagnosis of focal liver diseases based on deep learning technique for ultrasound images. Arab J Sci Eng, 2017, 42(8): 3127-3140.
15.	Huang Q, Zhang F, Li X. Machine learning in ultrasound computer-aided diagnostic systems: a survey. BioMed Res Int, 2018, 2018: 5137904-5137910.
16.	Sugimoto K, Shiraishi J, Moriyasu F, et al. Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography. Acad Radiol, 2009, 16(4): 401-411.
17.	Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med, 2019, 25(1): 70-74.
18.	Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
19.	陈平雁. 临床试验中样本量确定的统计学考虑. 中国卫生统计, 2015, 32(4): 727-731, 733.

1. Tang C, Fang K, Guo Y, et al. Safety of sulfur hexafluoride microbubbles in sonography of abdominal and superficial organs: retrospective analysis of 30, 222 cases. J Ultrasound Med, 2017, 36(3): 531-538.
2. Zhou LQ, Wang JY, Yu SY, et al. Artificial intelligence in medical imaging of the liver. World J Gastroenterol, 2019, 25(6): 672-682.
3. Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer, 2018, 18(8): 500-510.
4. 朱一丹, 李会娟, 武阳丰. 诊断准确性研究报告规范(STARD)2015介绍与解读. 中国循证医学杂志, 2016, 16(6): 730-735.
5. Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Clin Chem, 2015, 61(12): 1446-1452.
6. 刘丹, 周吉银. 临床科研项目样本量的要求. 中国医学伦理学, 2019, 32(6): 716-718, 723.
7. Buderer NM. Statistical methodology: I. Incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. Acad Emerg Med, 1996, 3(9): 895-900.
8. Baratloo A, Hosseini M, Negida A, et al. Part 1: simple definition and calculation of accuracy, sensitivity and specificity. Emerg (Tehran), 2015, 3(2): 48-49.
9. Negida A, Fahim NK, Negida Y. Sample size calculation guide - part 4: how to calculate the sample size for a diagnostic test accuracy study based on sensitivity, specificity, and the area under the ROC curve. Adv J Emerg Med, 2019, 3(3): e33.
10. Wu K, Chen X, Ding M. Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Intern J Light Elect Opt, 2014, 125(15): 4057-4063.
11. Guo LH, Wang D, Qian YY, et al. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clin Hemorheol Microcirc, 2018, 69(3): 343-354.
12. Malhotra RK, Indrayan A. A simple nomogram for sample size for estimating sensitivity and specificity of medical tests. Indian J Ophthalmol, 2010, 58(6): 519-522.
13. 倪延延, 张晋昕. 假设检验时样本含量估计中容许误差δ的合理选取. 循证医学, 2011, 11(6): 370-372.
14. Hassan TM, Elmogy M, Sallam E. Diagnosis of focal liver diseases based on deep learning technique for ultrasound images. Arab J Sci Eng, 2017, 42(8): 3127-3140.
15. Huang Q, Zhang F, Li X. Machine learning in ultrasound computer-aided diagnostic systems: a survey. BioMed Res Int, 2018, 2018: 5137904-5137910.
16. Sugimoto K, Shiraishi J, Moriyasu F, et al. Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography. Acad Radiol, 2009, 16(4): 401-411.
17. Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med, 2019, 25(1): 70-74.
18. Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
19. 陈平雁. 临床试验中样本量确定的统计学考虑. 中国卫生统计, 2015, 32(4): 727-731, 733.

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