Artificial Intelligence and Echocardiography

Abstract

Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed tomography, magnetic resonance, and radioisotope imaging, AI has influenced all steps of echocardiography, from image acquisition to automatic measurement and interpretation. Considering that echocardiography often is affected by inter-observer variability and shows a strong dependence on the level of experience, AI could be extremely advantageous in minimizing observer variation and providing reproducible measures, enabling accurate diagnosis. Currently, most reported AI applications in echocardiographic measurement have focused on improved image acquisition and automation of repetitive and tedious tasks; however, the role of AI applications should not be limited to conventional processes. Rather, AI could provide clinically important insights from subtle and non-specific data, such as changes in myocardial texture in patients with myocardial disease. Recent initiatives to develop large echocardiographic databases can facilitate development of AI applications. The ultimate goal of applying AI to echocardiography is automation of the entire process of echocardiogram analysis. Once automatic analysis becomes reliable, workflows in clinical echocardiographic will change radically. The human expert will remain the master controlling the overall diagnostic process, will not be replaced by AI, and will obtain significant support from AI systems to guide acquisition, perform measurements, and integrate and compare data on request.

Cardiac microstructural alterations measured by echocardiography identify sex-specific risk for heart failure

“…There have been multiple alternative approaches regarding echocardiographic texture analysis within the published literature. Although the integrated backscatter techniques we attempt to improve on account for most of the historical literature, deep learning approaches have been recently reported 29 30. With the expansion in deep learning technology, we expect that most will outperform the HS-SIC, given its simplicity.…”

Artificial intelligence for the echocardiographic assessment of valvular heart disease

“…During a routine echocardiogram, a large volume of potentially diagnostic data are generated, which are further increased with 3D imaging and speckle tracking strain analysis. The totality of data available can be difficult for the busy cardiologist to parse and interpret and are likely underutilised 36. It is unknown how many ‘hidden’ variables exist within an echocardiogram and AI can help discover the value of these variables 7.…”

“…The totality of data available can be difficult for the busy cardiologist to parse and interpret and are likely underutilised. 36 It is unknown how many ‘hidden’ variables exist within an echocardiogram and AI can help discover the value of these variables. 7 This is especially relevant when discussing VHD, as currently the assessment is predominantly focused on valve haemodynamics.…”

Echocardiographic Advances in Dilated Cardiomyopathy

“…Finally, a novel technique to evaluate diastolic function comes from AI. ML is a powerful tool able to process large datasets, like echocardiographic parameters of diastolic function, combined with detailed clinical and demographic features [ 86 ]. While E/A ratio has the limitation of a well-known U-shaped relationship with LV filling pressure, ML is particularly well-suited to detect and describe non-linear relationships [ 87 ], which is pertinent to diastolic assessment.…”