Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions

Abstract

Echocardiography (echo) is a critical tool in diagnosing various cardiovascular diseases. Despite its diagnostic and prognostic value, interpretation and analysis of echo images are still widely performed manually by echocardiographers. A plethora of algorithms has been proposed to analyze medical ultrasound data using signal processing and machine learning techniques. These algorithms provided opportunities for developing automated echo analysis and interpretation systems. The automated approach can significantly assist in decreasing the variability and burden associated with manual image measurements. In this paper, we review the state-of-the-art automatic methods for analyzing echocardiography data. Particularly, we comprehensively and systematically review existing methods of four major tasks: echo quality assessment, view classification, boundary segmentation, and disease diagnosis. Our review covers three echo imaging modes, which are B-mode, M-mode, and Doppler. We also discuss the challenges and limitations of current methods and outline the most pressing directions for future research. In summary, this review presents the current status of automatic echo analysis and discusses the challenges that need to be addressed to obtain robust systems suitable for efficient use in clinical settings or point-of-care testing.

Fig. 2.

M-mode echocardiography recorded from the first author. IVS: Inter-ventricular Septum; PW: Posterior Wall; IDs: Internal Diameter (systole); IDd: Internal Diameter (diastole); green curve is ECG signal.

Fig. 3.

B-mode A4C view echocardiography.

Fig. 4.

Spectral Doppler mode echocardiography (mitral valve [MV] flow). The upper part of the image shows the color Doppler. Image is taken from Open-i.

Fig. 1.

Flowchart of our review. The histogram associated with each mode represents the number of automated works for each task.