Using artificial intelligence for rheumatic heart disease detection by echocardiography: Focus on mitral

Using artificial intelligence for rheumatic heart disease detection by echocardiography: Focus on mitral

Reviewer: Dr Lucy Law| ASA SIG: Cardiac

Authors: Brown K, Roshanitabrizi P, Rwebembera J, Okello E, Beaton A, Linguraru M, Sable CReviewer: Dr Lucy Law| ASA SIG: Cardiac

Why the study was performed

Studies have shown that, by task-shifting screening echocardiography to non-expert operators, increased early detection of latent rheumatic heart disease (RHD) is possible. However, the issues regarding the lack of specialised personnel to diagnose RHD from these assessments remain. This study aimed to use deep and machine learning artificial intelligence (AI) methods to evaluate if the detection of RHD using mitral regurgitation (MR) criteria was comparatively accurate to expert cardiologists.

How the study was performed

Echocardiographic data used in this study was collected by experts using a specific imaging protocol and was part of a 2-year control trial study assessing the impact of secondary antibiotic prophylaxis in Ugandan adolescents and children with latent RHD. Using the 2012 World Heart Federation (WHF) criteria a panel of experts noted the presence or absence of RHD. Cases that were diagnosed using non-colour Doppler MR criteria, including spectral Doppler and morphological features, were excluded. In total, 511 (229 without RHD) were included in this study.

Multiple techniques were used to test and train AI algorithms. Localisation of the left atrium (LA) as well as identification of systolic frames was achieved by image harmonisation and convolutional neural networks. These processes were applied to address the inherent image differences that can occur such as spatial resolution and lack of electrocardiogram (ECG) trace. Subsequently, the machine learning approach was used to analyse MR jet length. Cross-validation and linear support vectors were used to identify 9 features that best describe and delineate the MR jet. The results of these methods were then compared to that of expert reviewers. The deep learning approach was also applied as an attempt to integrate intelligent image analysis which goes beyond factors identified by expert operators. This was achieved by using two deep learning models specialised in processing multi-view (in this case parasternal long axis (PLAX) and apical 4 chamber (A4C) views) data; one dealing with special and temporal factors, and the other interframe temporal relationships.

What the study found

Image harmonisation for identification of the LA, from the PLAX and A4C views, had an accuracy of 0.99 and selected the correct systolic frame over 92% of the time. Localisation of the LA using the Dice coefficient exceeded 0.85 for both the PLAX and A4C views.

Machine learning techniques measuring MR jet length showed no statistically significant difference when compared to manual measurements performed by experts, with further analysis showing strong agreement between the methods. The combined deep learning methods performed best showing a precision of 0.78 and a diagnostic performance of 0.84 (De Long’s method).

Relevance to clinical practice

Using AI to analyse and interpret ultrasound images should not be done just because it is available but should add value to the process and help validate and define its place within sonography.

The authors have applied AI technologies to address a significant gap in service provision, namely the lack of the specialist’s capacity to review and diagnose RHD. The use of such technology in RHD endemic areas, which are under-resourced and low-middle income, could significantly aid in decreasing the global burden of RHD.

“Artificial intelligence (AI) could have important implications for scaling rheumatic heart disease (RHD) detection in endemic regions by nonexpert healthcare workers”