Artificial intelligence for the echocardiographic assessment of valvular heart disease

Abstract

Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.

Inferior vena cava distensibility from subcostal and trans-hepatic imaging using both M-mode or artificial intelligence: a prospective study on mechanically ventilated patients.

“…The use of AI has been applied to the whole echocardiography setting (i.e. left ventricular systolic 23,24 and diastolic [25][26][27] function, to right ventricular function 28 , assessment of heart valve diseases 29 , diagnosis of congenital heart diseases 30 ) and also to predict of FR, with encouraging results. For instance, Bataille et al 31 showed that machine learning models predicted FR with comparable accuracy to the hemodynamic response to passive leg raising, and evaluation of the IVC was among the key variables identified by the model, together with other Doppler derived parameters.…”

“…Among these, also echocardiography is experiencing a significant expansion of AI applications that might help daily practice. Indeed, AI has been used for the assessment of left ventricular systolic 23,24 and diastolic [25][26][27] function, right ventricular function 28 , but also for the evaluation of heart valve 29 and congenital heart diseases 30 . Moreover, machine learning has been developed for predicting FR at patient's bedside 31 with preliminary data on the implementation of AI for IVC assessment 32 .…”

The Role of Artificial Intelligence in Echocardiography

“…The rationale behind the use of AI in echocardiography to identify disease states is based on its capacity to automatically analyze features from images and data that are beyond human perception [ 36 ]. During routine echocardiography, a huge volume of potentially diagnostic information could be underutilized, considering that the totality of data generated can be hard to interpret by human experts in a short time period [ 37 ].…”

Assessment of the inferior vena cava collapsibility from subcostal and trans-hepatic imaging using both M-mode or artificial intelligence: a prospective study on healthy volunteers.

“…Among these, also echocardiography is experiencing a signi cant implementation of AI functions that may aid and/or simplify clinicians' work. For instance, AI has been applied to the estimation of left ventricular systolic [24,25] and diastolic[26-28] function, to right ventricular function [29], but it has also been adopted for the assessment of heart valve diseases [30,31] and for diagnosis of congenital heart diseases [32]. Further, machine learning methods have been developed for the improvement of bedside prediction of FR [33], and preliminary experiences with AI in the assessment of IVCc have been reported [34].…”