Role of Artificial Intelligence in Echocardiography: A Narrative Review

Q: What is the clinical evidence from this study?

Study design: Review. Population: Cardiovascular disease evaluated by echocardiography. Intervention: Artificial intelligence (machine learning and deep learning) in echocardiography vs. Conventional manual echocardiographic acquisition and interpretation by human experts.

Minati Choudhury

doi:10.5005/jp-journals-10034-1131

February 20, 2024Journal of Perioperative EchocardiographyOpen Access

Role of Artificial Intelligence in Echocardiography: A Narrative Review

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Key result

Artificial intelligence enables automated echocardiographic image acquisition, view recognition, segmentation, and interpretation with high accuracy.

Key Points

To review the evolving applications, clinical utility, and ongoing challenges of artificial intelligence and machine learning in echocardiographic imaging.
Conducted a narrative review synthesizing literature retrieved from a PubMed/Medline search covering 2019 to 2022 using the search terms 'AI', 'machine learning (ML)', and 'echocardiography'.
Evaluated core artificial intelligence subfields, including deep learning, neural networks, and computer vision, applied to echocardiographic workflows.
Convolutional neural networks automate image acquisition, view recognition, and cardiac chamber segmentation across sequential scanning layers ranging from boundary detection to complex pattern recognition.
Machine learning models facilitate instant processing of complex data to detect non-linear associations, assist outcome prediction, and optimize image quality in real time.
Key implementation challenges include algorithmic vulnerability to grainy or biased data, requiring strict data management standards and continuous expert human oversight.

Structured PICO

P

Population

Patients undergoing echocardiography for diagnostic and prognostic evaluation of cardiovascular diseases, particularly valvular heart disease, across reviewed studies from 2019 to 2022.

I

Intervention

Artificial intelligence (AI), machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs) applied to echocardiography

C

Comparator

Conventional manual echocardiographic image acquisition, measurement, and interpretation by human echocardiographers

Limitations

Deep gap between AI-based research studies and routine clinical application in echocardiography
Paucity of literature describing AI identification of specific valve anatomy and Doppler quantification
Programs for assessment of pulmonary valve stenosis or regurgitation are yet to be developed
Grainy or biased input data lead to poor quality interpretation despite ideal algorithms
Lack of standardized protocols for AI data management
Poor quality of interpretation when applied to grainy or biased data despite an ideal algorithm
Lack of a set of standards for AI data management
Program is yet to be developed for the assessment of pulmonary valve stenosis or regurgitation
Paucity of literature describing the identification of specific valve anatomy and Doppler quantification

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Implication

This narrative review highlights that artificial intelligence, including machine learning and deep learning, offers promising automation in echocardiographic image acquisition, segmentation, view identification, and disease interpretation to reduce clinician workload and improve diagnostic accuracy.

Abstract

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Cite This Study

Minati Choudhury (2024) conducted a review in Cardiovascular disease evaluated by echocardiography. Artificial intelligence (machine learning and deep learning) in echocardiography vs. Conventional manual echocardiographic acquisition and interpretation by human experts was evaluated. Artificial intelligence enables automated echocardiographic image acquisition, view recognition, segmentation, and interpretation with high accuracy.

synapsesocial.com/papers/6a78b649f6b553d308af5190 https://doi.org/10.5005/jp-journals-10034-1131

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Role of Artificial Intelligence in Echocardiography: A Narrative Review

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Role of Artificial Intelligence in Echocardiography: A Narrative Review

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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