Deep learning supported echocardiogram analysis: A comprehensive review.

An echocardiogram is a sophisticated ultrasound imaging technique employed to diagnose heart conditions. The transthoracic echocardiogram, one of the most prevalent types, is instrumental in evaluating significant cardiac diseases. However, interpreting its results heavily relies on the clinician's expertise. In this context, artificial intelligence has emerged as a vital tool for helping clinicians. This study critically analyzes key state-of-the-art research that uses deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments. We have systematically organized and categorized articles that proffer solutions for view classification, enhancement of image quality and dataset, segmentation and identification of cardiac structures, detection of cardiac function abnormalities, and quantification of cardiac functions. We compared the performance of various deep learning approaches within each category, identifying the most promising methods. Additionally, we highlight limitations in current research and explore promising avenues for future exploration. These include addressing generalizability issues, incorporating novel AI approaches, and tackling the analysis of rare cardiac diseases.

Automatic Diagnostic Tool for Detection of Regional Wall Motion Abnormality from Echocardiogram.

The echocardiogram is an ultrasound imaging modality, employed to assess cardiac abnormalities. The Regional Wall Motion Abnormality (RWMA) is the occurrence of abnormal or absent contractility of a region of the heart muscle. Conventional assessment of RWMA is based on visual interpretation of endocardial excursion and myocardial thickening from the echocardiogram videos. Wall motion assessment accuracy depends on the experience of the sonographer. Current automated methods highly depend on the preprocessing steps such as segmentation of ventricle part or manually finding systole and diastole frames from an echocardiogram. Additionally, state-of-the-art methods majorly make use of images rather than videos, which specifically lack the usage of temporal information associated with an echocardiogram. The deep learning models used, employ highly complex networks with billions of trainable parameters. Further, the existing models used on video data add to the computational intensity because of the high frame rates of echocardiogram videos. We developed a novel deep learning architecture EC3D-Net (Echo-Cardio 3D Net), which captures the temporal information for identifying regional wall motion abnormality from echocardiogram. We demonstrate that EC3D-Net can extract temporal information from even raw echocardiogram videos, at low frame rates, employing minimal training parameter-based deep architecture. EC3D-Net achieves both an overall F1-Score and an Area Under Curve (AUC) score of 0.82. Further, we were able to reduce time for training and trainable parameters by 50% through minimizing frames per second. We also show the EC3D-Net is an interpretable model, thereby helping physicians understand our model prediction. RWMA detection from echocardiogram videos is a challenging process and our results demonstrate that we could achieve the state-of-the-art results even while using minimal parameters and time by our EC3D-Net. The proposed network outperforms both complex deep networks as well as fusion methods generally used in video classification.