Automated assessment of transthoracic echocardiogram image quality using deep neural networks

作     者：Robert B.Labs Apostolos Vrettos Jonathan Loo Massoud Zolgharni Robert B. Labs;Apostolos Vrettos;Jonathan Loo;Massoud Zolgharni

作者机构：School of Computing and EngineeringUniversity of West LondonLondonUnited Kingdom Imperial CollegeHealthcareNHS TrustUnited Kingdom 

出 版 物：《Intelligent Medicine》 (智慧医学(英文))

年 卷 期：2023年第3卷第3期

页      面：191-199页

核心收录：

学科分类：12[管理学] 08[工学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Robert Labs: original draft preparation, data curation, writing Apostolos Vrettos: ground truth annotations Jonathan Loo: validation, reviewing Massoud Zolgharni: reviewing, formatting, editing 

主　　题：Image quality Echocardiography Objective assessment Deep learning Ultrasound 

摘      要：Background Standard views in two-dimensional echocardiography are well established but the qualities of acquired images are highly dependent on operator skills and are assessed *** study was aimed at providing an objective assessment pipeline for echocardiogram image quality by defining a new set of domain-specific quality ***,image quality assessment can thus be automated to enhance clinical measurements,interpretation,and real-time *** We developed deep neural networks for the automated assessment of echocardiographic frames that were randomly sampled from 11,262 adult *** private echocardiography dataset consists of 33,784 frames,previously acquired between 2010 and *** non-medical images where full-reference metrics can be applied for image quality,echocardiogram s data are highly heterogeneous and requires blind-reference(IQA)***,deep learning approaches were used to extract the spatiotemporal features and the image s quality indicators were evaluated against the mean absolute *** quality indicators encapsulate both anatomical and pathological elements to provide multivariate assessment scores for anatomical visibility,clarity,depth-gain and *** The model performance accuracy yielded 94.4%,96.8%,96.2%,97.4%for anatomical visibility,clarity,depth-gain and foreshortedness,*** mean model error of 0.375±0.0052 with computational speed of 2.52 ms per frame(real-time performance)was *** The novel approach offers new insight to the objective assessment of transthoracic echocardiogram image quality and clinical quantification in A4C and PLAX *** also lays stronger foundations for the operator s guidance system which can leverage the learning curve for the acquisition of optimum quality images during the transthoracic examination.