Diagnostic efficacy of artificial intelligence model based on yolox framework integrating left ventricular segmentation and key point detection to automatically measure left ventricular ejection function in patients with chronic renal failure
10.3760/cma.j.cn131148-20231214-00291
- VernacularTitle:基于yolox框架整合左心室分割和关键点检测的人工智能模型评估慢性肾衰竭患者左心室收缩功能
- Author:
Hanxiao LI
1
;
Qiang JI
;
Yang ZHAO
;
Chuang JIA
;
Shujiao JI
;
Jianjun YUAN
;
Yu XING
;
Tian ZENG
;
Haohui ZHU
Author Information
1. 新乡医学院，新乡 453000
- Keywords:
Echocardiography;
Artificial intelligence;
Left ventricular ejection function;
TomTec software;
Chronic renal failure
- From:
Chinese Journal of Ultrasonography
2024;33(5):407-414
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the detection performance of left ventricular ejection fraction (LVEF) in patients with chronic renal failure (CRF) by an artificial intelligence (AI) model based on yolox framework integrating left ventricular segmentation and critical point detection.Methods:From January 2019 to June 2023, a total of 4 284 echocardiographic images of 2 000 adults aged 18-80 years without segmental wall motion abnormalities, structural heart disease, cardiac surgery or cardiomyopathy were collected in Henan Provincial People′s Hospital to delineate the endocardial membrane, as a training set, an AI model based on yolox framework integrating left ventricular segmentation and critical point detection was established. The images were divided into the training set( n=1 675) and the test set( n=325) in a ratio of about 5∶1. All 228 echocardiographic images of 100 normal adult volunteers who were treated in Henan Provincial Chest Hospital from May 2020 to May 2021 were collected as external test set validation. All 792 echocardiographic images of 204 patients treated in Henan Provincial People′s Hospital from April 2019 to June 2023 were continuously enrolled to evaluate the measurement efficiency of AI model. Spearman correlation statistical method was used to analyze the consistency of AI model measurement with manual measurement and TomTec software measurement methods of 3 senior echocardiographic professionals. Subjects were divided into clear image group, unclear image group, normal LVEF group and reduced LVEF group, the differences of general data between the two groups were compared. The correlation coefficient(ICC) within the group was calculated to analyze the consistency, so as to evaluate the model performance. Results:LVEF measured by AI model was significantly correlated with both manual measurement and TomTec model measurement ( rs=0.834, 0.826; all P<0.01). ICC values of the clear image group and the unclear image group were 0.96 and 0.97, respectively. ICC values for all subjects, normal LVEF group and reduced LVEF group were 0.96, 0.90 and 0.96, respectively. Conclusions:The AI model based on yolox framework integrating left ventricular segmentation and critical point detection has good diagnostic performance in the automatic measurement of LVEF in patients with CRF.
