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Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

Background Brugada syndrome is an inherited cardiac channelopathy associated with major arrhythmic events (MAEs). The presence of a positive family history of sudden cardiac death (SCD) as a risk predictor of MAE remains controversial. We aimed to examine the association between family history of SCD and MAEs stratified by age of SCD with a systematic review and meta‐analysis. Methods and Results We searched the databases of MEDLINE and EMBASE from January 1992 to January 2020. Data from each study were combined using the random‐effects model. Fitted metaregression was performed to evaluate the association between the age of SCD in families and the risk of MAE. Twenty‐two studies from 2004 to 2019 were included in this meta‐analysis involving 3386 patients with Brugada syndrome. The overall family history of SCD was not associated with increased risk of MAE in Brugada syndrome (pooled odds ratio [OR], 1.11; 95% CI, 0.82–1.51; P =0.489, I 2 =45.0%). However, a history of SCD in family members of age younger than 40 years of age did increase the risk of MAE by ≈2‐fold (pooled OR, 2.03; 95% CI, 1.11–3.73; P =0.022, I 2 =0.0%). When stratified by the age of cut point at 50, 45, 40, and 35 years old, a history of SCD in younger family member was significantly associated with a higher risk of MAE (pooled OR, 0.49, 1.30, 1.51, and 2.97, respectively; P =0.046). Conclusions A history of SCD among family members of age younger than 40 years was associated with a higher risk of MAE.

Aims:Increased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results: Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 and 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4-chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: HCM: 0.93, CA: 0.90, and HTN/other: 0.92). Conclusion: The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.