Real-time echocardiography image analysis and quantification of cardiac indices

Rajaraman, Sivaramakrishnan; Hsu, Li‐Yueh; Sachdev, Vandana; Antani, Sameer

doi:10.1016/j.media.2022.102438

Cited by 27 publications

(13 citation statements)

References 42 publications

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“…The authors achieved an overall accuracy of 88.9% for flow QA. Later, in ( 19 ), they used a different approach in TTE by developing a lightweight model (MobileNetV2-s) for retrieving echocardiograms of acceptable quality, thus automating the process of excluding low-quality echocardiograms performed by echocardiographers in clinical practice. Prior to training, the authors used self-supervised representation to learn low-level features.…”

Section: Artificial Intelligence Applications For Echocardiography Ac...mentioning

confidence: 99%

Assisted probe guidance in cardiac ultrasound: A review

Ferraz

Coimbra²,

Pedrosa³

2023

Front. Cardiovasc. Med.

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Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator’s experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.

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Section: Artificial Intelligence Applications For Echocardiography Ac...mentioning

confidence: 99%

Assisted probe guidance in cardiac ultrasound: A review

Ferraz

Coimbra²,

Pedrosa³

2023

Front. Cardiovasc. Med.

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“…In addition, we aimed for research articles that employed substantial amounts of data, rather than works based on a limited pool of evidence. As we are not limited to one event or disease type (e.g., Tuberculosis [4], lung cancer, cardiac diseases [5], and COVID-19 [6], [7]), the proposed SI aimed at attracting many submissions, ranging from screening to diagnosis, prognosis, and surgery/treatment plans.…”

Section: Guest Editorial Multimodal Learning In Medicalmentioning

confidence: 99%

Guest Editorial Multimodal Learning in Medical Imaging Informatics

Santosh

2023

IEEE J. Biomed. Health Inform.

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“…On the other hand, several methods also have been proposed to develop lightweight networks to fulfill the real-time requirement. A recently proposed method (Zamzmi et al 2022) transfers relevant knowledge for fine-tuning on top of a lightweight framework to enhance generalization and speed up convergence. But it failed to exploit temporal relationships between video frames and hence still cannot achieve satisfactory accuracy.…”

Section: Exploit the Inherent Characteristics Of 2d Or 3d Convolution...mentioning

confidence: 99%

Super-efficient Echocardiography Video Segmentation via Proxy- and Kernel-Based Semi-supervised Learning

Lin

Xie

et al. 2023

AAAI

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Automatic segmentation of left ventricular endocardium in echocardiography videos is critical for assessing various cardiac functions and improving the diagnosis of cardiac diseases. It is yet a challenging task due to heavy speckle noise, significant shape variability of cardiac structure, and limited labeled data. Particularly, the real-time demand in clinical practice makes this task even harder. In this paper, we propose a novel proxy- and kernel-based semi-supervised segmentation network (PKEcho-Net) to comprehensively address these challenges. We first propose a multi-scale region proxy (MRP) mechanism to model the region-wise contexts, in which a learnable region proxy with an arbitrary shape is developed in each layer of the encoder, allowing the network to identify homogeneous semantics and hence alleviate the influence of speckle noise on segmentation. To sufficiently and efficiently exploit temporal consistency, different from traditional methods which only utilize the temporal contexts of two neighboring frames via feature warping or self-attention mechanism, we formulate the semi-supervised segmentation with a group of learnable kernels, which can naturally and uniformly encode the appearances of left ventricular endocardium, as well as extracting the inter-frame contexts across the whole video to resist the fast shape variability of cardiac structures. Extensive experiments have been conducted on two famous public echocardiography video datasets, EchoNet-Dynamic and CAMUS. Our model achieves the best performance-efficiency trade-off when compared with other state-of-the-art approaches, attaining comparative accuracy with a much faster speed. The code is available at https://github.com/JingyinLin/PKEcho-Net.

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Real-time echocardiography image analysis and quantification of cardiac indices

Cited by 27 publications

References 42 publications

Assisted probe guidance in cardiac ultrasound: A review

Assisted probe guidance in cardiac ultrasound: A review

Guest Editorial Multimodal Learning in Medical Imaging Informatics

Super-efficient Echocardiography Video Segmentation via Proxy- and Kernel-Based Semi-supervised Learning

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