Objective To explore the diagnostic value of ultra-high resolution contrast-enhanced ultrasound(UHRUS)and conventional contrast-enhanced ultrasound(CEUS)in lymph node tuberculosis with the help of machine learning.Methods Prospective collection of 198 patients with cervical lymphadenopathy who visited Hangzhou Red Cross Hospital from January 2021 to January 2024,and randomly divided them into a training set and a validation set in a 7:3 ratio.Normal CEUS model and HR CEUS model were established using machine learning methods,and the diagnostic efficacy of the two models was compared and analyzed.Results The area under the curve(AUC)of the normal CEUS model in the training set and validation set are 0.820 and 0.798,respectively.The AUC(0.993 and 0.990)of the HR CEUS model in the training and validation sets were higher than those of the normal CEUS model,and its specificity(100％)in the validation set was also higher than that of the normal CEUS model(60.9％).Conclusion The ultra-high resolution CEUS model based on machine learning has better diagnostic value than the conventional CEUS model.
Ultra-high resolution ultrasoundContrast-enhanced ultrasoundMachine learningTuberculosis of lymph nodes
万方数据
维普
