Abstract
Abstract Background Until now, no method is available for automatic extraction of indicators of image quality and view standardization from echocardiographic recordings. Thus, description of data quality has been indirect, often by description of operators experience and the methodology used. Peak systolic global longitudinal strain (GLS) is a sensitive measure of left ventricular (LV) function, still hampered by random variability due to suboptimal standardization of recordings and variability in data processing by the operators. Data on how acquisitions and data processing influence GLS and its variability are scarce. Objectives Aim was to study the importance of image acquisition and data processing by experienced operators for GLS in a large healthy population by automatically extracting quality indicators using novel deep learning based image analysis software and vendor specific metadata. Secondly, we aimed to study how these characteristics influenced the reference ranges and GLS variability. Methods Participants from a large echocardiographic study were included. Echocardiography was performed according to current recommendations. Two experienced operators and two expert cardiologists read and re-read all GLS recordings (1,412 paired analyses). Acquisition- and data processing characteristics were extracted using deep learning and GLS software metadata providing specific data on position of landmarks in the view, as well as rotation and tilt of the cut-plane according to the best possible standard. From these measurements several quality indicators were provided (Table 1). Results Mean age for the 1,412 participants was 58±12 years and 56% were women. Averaged apical LV foreshortening in the recordings was ≤2 mm and the view specific recordings were well standardized, with high alignment with the preferred cut-plane for tilt and rotation across the three apical views (Figure 1). Most quality indicators influenced GLS and absolute GLS was lower with longer LVs (-0.2% per 1 cm, p=0.003) and longer distance from the transducer to the LV apex (-0.3% per 1 cm, p=0.02) in the recordings, and also for wider regions of interest during data processing (-0.7% per 1 mm, p <0.001). Absolute GLS was 0.7% higher per mm systolic foreshortening of the apical region of interest, and when more knots were placed under initialization of the ROI (0.1% per knot, p=0.004). In test-retest analyses, the results for agreement between operators followed the above presented data. Conclusions Novel quality indicators were successfully extracted using novel DL software and strain specific metadata. Even though acquisitions and data processing were well standardized, several quality indicators from both steps influenced GLS. Deep learning software and data processing metadata may provide quality indicators of the echocardiographic databases of importance to interpret the study outcomes and motivate researchers to optimize echocardiographic acquisitions and analyses.