Real-time Automatic Feedback by Deep Learning to Reduce Apical Foreshortening in Echocardiography

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority Background Left ventricular (LV) foreshortening is common in echocardiography and may impair reproducibility and estimation of LV function. Feedback of foreshortening metrics such as LV length during scanning could improve accuracy, however, such a tool has not been available. Purpose To evaluate the effect of feedback of LV length during imaging for experienced sonographers, measured in real-time using a robust deep learning (DL) based tool. Methods Consecutive patients (n = 108) with mixed cardiac pathology were included during two separate periods. Each patient underwent three echocardiograms. The first and second examinations were performed by two (of three) randomized sonographers and the third exam by one (of four) randomized cardiologists. All examinations included the three standard apical views and the cardiologists’ exam included tri-plane recordings for reference. The data collection was divided in two phases. In the first period, the sonographers were told to provide high quality echocardiograms for analyses of LV function. Subsequently, sonographers were trained in use of the real-time DL tool on 10 patients each. In period two, the algorithm was used during scanning by the sonographer performing the second exam. One expert reader measured LV length (subendocardial apex to mitral annular plane) in all exams. The cardiologists’ tri-plane recordings were analyzed and used as reference. LV foreshortening was calculated at end-diastole (reference minus the operators LV length). Each exam was classified as foreshortened if the difference was ≥4 mm. Results After excluding those not in sinus rhythm (n = 9) and with need for contrast echocardiography (n = 11), 88 patients (45% women) were included (41 in period 1 and 47 in period 2). Age was mean (SD) 63 (16) years (45% women). Main findings are shown in the Table. LV length by both sonographer groups was significantly foreshortened in period 1 (mean 2.6 and 3.5 mm), and both sonographer groups reduced LV foreshortening in period 2 (both p ≤0.02). Improvement was best for sonographers using the real-time DL algorithm. These were not significantly foreshortened compared to the reference (p = 0.53), and less foreshortened compared to sonographer 1 (p = 0.001). Similarly, sonographers had more foreshortened examinations than cardiologist in period 1 (sonographer groups; 15 (37%) and 13 (32%) vs. cardiologist; 5 (12%), both p ≤0.03). In period 2, there was no difference in proportion of foreshortening between cardiologists and sonographer 2 (cardiologist; 0 vs sonographer; 2 (4%, p = 0.5)), while sonographer 1 foreshortened more 11 (23%), p ≤0.003). Conclusion Feedback of LV length during imaging significantly reduced foreshortening in echocardiographic acquisitions by sonographers, and has the potential to improve image accuracy even in the hands of experienced operators. Abstract Table Abstract Figure. Deep Learning foreshortening application