This method is simple and intuitive, and the clinical accuracy of the analysis method can be observed through the chart, and the judgment can be made by the naked eye. ROC curve combines sensitivity and specificity in a graphical way, which can accurately reflect the relationship between specificity and sensitivity of an analysis method and is a comprehensive representative of test accuracy. ROC curve does not fix the classification boundary value, but allows the existence of intermediate state, which is beneficial for users to combine professional knowledge, weigh the influence of missed diagnosis and misdiagnosis, and choose a better tangent point as the diagnostic reference value. It provides an intuitive comparison between different tests under a common scale. The more convex the ROC curve is, the closer it is to the upper left corner, indicating that its diagnostic value is greater, which is beneficial to the comparison between different indexes. The area under the curve can evaluate the diagnostic accuracy.