Deep learning algorithm based on artificial intelligence to assist the diagnosis of early ESCC
Objective To establish an artificial intelligence-aided diagnosis model to improve the detection rate of early ESCC.Methods White light images,iodine staining images and complete videos of early ESCC,ESCC,esopha-geal protuberant lesions and esophageal diverticulum were selected from 3 digestive endoscopic centers of the 988th Hos-pital of PLA Joint Logistics Support Force,the 984th Hospital of PLA Joint Logistics Support Force,and the 980th Hos-pital of PLA Joint Logistics Support Force from Jun.2018 to Jun.2020.The lesions in the picture were marked with rectangles and polygons,which were divided into training set,verification set and test set.Through training and verif-ying different target detection models and case segmentation models,the best target detection model Yolov 5 and case segmentation model Yolact++ were selected to construct AI ″chimera model″.Finally,the performance of AI ″chimeric model″ in the diagnosis of early ESCC was evaluated.Results The sensitivity,specificity and accuracy of AI ″chimeric model″ in the diagnosis of early ESCC were 95.60%,91.60%and 90.70%,respectively.Conclusion The AI ″chime-ric model″ constructed in this study can significantly improve the detection rate of early ESCC.
NETL
NSTL
万方数据
维普
