Hierarchical Vision Transformers for Cardiac Ejection Fraction Estimation

Fazry, Lhuqita; Haryono, Asep; Nissa, Nuzulul Khairu; Sunarno, _; Hirzi, Naufal Muhammad; Rachmadi, Muhammad Febrian; Jatmiko, Wisnu

doi:10.1109/iwbis56557.2022.9924664

Cited by 12 publications

(2 citation statements)

References 15 publications

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“…Fazry and his team [ 66 ] introduced a new deep learning method for estimating the ejection fraction from echocardiogram videos, eliminating the need for left-ventricle segmentation. This approach, known as UltraSwin, leverages hierarchical vision transformers and Swin transformers to extract spatio-temporal features.…”

Section: Organsmentioning

confidence: 99%

Ultrasound Image Analysis with Vision Transformers—Review

Vafaeezadeh,

Behnam,

Gifani

2024

Diagnostics

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Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges, such as a low imaging quality and high variability. There is a need to develop advanced automatic US image analysis methods to enhance its diagnostic accuracy and objectivity. Vision transformers, a recent innovation in machine learning, have demonstrated significant potential in various research fields, including general image analysis and computer vision, due to their capacity to process large datasets and learn complex patterns. Their suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformers and discusses their applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in their application in medical US image analysis. Vision transformers have shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and are expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.

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Section: Organsmentioning

confidence: 99%

Ultrasound Image Analysis with Vision Transformers—Review

Vafaeezadeh,

Behnam,

Gifani

2024

Diagnostics

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“…This study contributes a novel tool for cardiac imaging and opens new possibilities for early detection and interventions in myocardial injuries. In another advancement, Lhuqita Fazry et al [197] developed a groundbreaking approach using hierarchical Vision Transformers to estimate cardiac ejection fraction from echocardiogram videos. Addressing the variability in ejection fraction assessment among different observers, this method does not require prior segmentation of the left ventricle, making it a more efficient process.…”

Section: Transformersmentioning

confidence: 99%

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Rudnicka,

Proniewska,

Perkins

et al. 2024

Electronics

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Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

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Predicting Ejection Fractions from Echocardiogram Videos Using Deep Learning

Hassan,

Obied

2024

Lecture Notes in Networks and Systems

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Hierarchical Vision Transformers for Cardiac Ejection Fraction Estimation

Cited by 12 publications

References 15 publications

Ultrasound Image Analysis with Vision Transformers—Review

Ultrasound Image Analysis with Vision Transformers—Review

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Predicting Ejection Fractions from Echocardiogram Videos Using Deep Learning

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