Deep Learning Based Cardiac Phase Detection Using Echocardiography Imaging

Farhad, M. M.; Masud, Mohammad Mehedy; Beg, Azam

doi:10.1007/978-3-030-95405-5_1

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Machine learning and deep learning methods [10] have been extensively applied for spatiotemporal feature extraction in echocardiography to enable intelligent assessment of cardiac function. These methods mainly include CNNs [7], RNNs, transformers [8] and 3DCNNs [9].…”

Section: Cardiac Function Assessmentmentioning

confidence: 99%

“…For instance, Farhad et al designed a specialized 9-layer CNN architecture for cardiac phase detection, effectively categorizing input images into end-diastole (ED), end-systole (ES), or non-ES/ED phases [7]. Reynaud et al [17] and Kang et al [18] also employed CNN in their models.…”

Section: Cardiac Function Assessmentmentioning

confidence: 99%

“…Various deep learning models employing different feature extraction networks have been widely utilized in the evaluation of echocardiograms. For example, convolutional neural networks (CNNs) [7], recurrent neural networks (RNN), transformers [8], and 3D convolutional neural networks (3DCNN) [9] have demonstrated exceptional capabilities in the automated analysis of echocardiograms, excelling in the extraction and analysis of complex features. These models provide an objective perspective, aiding healthcare professionals in promptly and accurately assessing cardiac function, thereby significantly enhancing objectivity and precision [10].…”

Section: Introductionmentioning

confidence: 99%

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An AI-based multitask framework for cardiac function assessment through echocardiograms

Guo,

Mo,

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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The accurate assessment of cardiac function is crucial for preventing and controlling cardiovascular diseases and reducing global mortality rates. In recent years, the rapid advancement of machine learning and deep learning, particularly the utilization of artificial intelligence technologies such as convolutional neural networks and multi-task learning, has significantly improved the objectivity and precision of assessing echocardiogram images. However, existing methods lack a thorough exploration of the intrinsic relationship between ejection fraction (EF), end-diastolic volume (EDV), and end-systolic volume (ESV) calculations, which ultimately influence the accuracy of cardiac function assessment. Therefore, we propose an AI-Based Multi-task Framework for Cardiac Function Assessment through echocardiograms. The framework utilizes a 3DCNN network to concurrently extract spatial and temporal features from echocardiographic videos. It employs multi-task learning by assigning varying weights to sub-tasks, enhancing the prediction accuracy of ejection fraction through joint training. Experimental results on the publicly available Echonet-Dynamic dataset demonstrate that the proposed framework achieves promising performance in ejection fraction prediction, with mean absolute error, root mean square error, and R² scores of 3.89%, 5.13%, and 0.82, respectively, surpassing other comparative methods. This framework will further aid clinicians in more accurate cardiac function assessment, offering promising prospects for its practical application.

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Section: Cardiac Function Assessmentmentioning

confidence: 99%

Section: Cardiac Function Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An AI-based multitask framework for cardiac function assessment through echocardiograms

Guo,

Mo,

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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show abstract

“…On a private dataset, they reported an aaFD of 1.59 frames for ED and 1.56 for ES phase. Farhad et al [22] presented a customized CNN architecture (DeepPhase) for detecting ED, ES, and non-EDES phases from echo sequences without left ventricle segmentation on echocardiograms. Their model was trained and tested on the CAMUS and new cardiacPhase datasets.…”

Section: Introductionmentioning

confidence: 99%

“…The preprocessing is followed by an echo phase detection as the binary classification (Diastole and Systole Phase) problem. The binary classification overcomes the problem of class imbalance in the classification (ED, ES, and non-EDES) problem formulation [18], [20], [21], and [22]. In binary classification, both phases carry approximately the same number of frames in an echo sequence, effectively addressing the class imbalance problem.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing and Frame Level Classification Framework for Cardiac Phase Detection in 2D Echocardiography

Singh,

Darji,

Sarvaiya

et al. 2023

Preprint

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The first and critical step of accurate heart function analysis (i.e., ventricles volume, Ejection Fraction, and stroke volume measurement) in echocardiography is the detection of end-diastole (ED) and end-systole (ES) frames. Detecting these frames is challenging due to variations in cardiac structure, heart rate associated with clinical conditions, and the low-resolution nature of echo sequences. Several deep learning techniques have recently emerged, primarily combining convolutional neural networks (CNN) and recurrent neural networks (RNN). These models were trained on a range of datasets, including open-access sources like CAMUS, PACS, and EchoNet-Dynamic, as well as private datasets. The largest open-access dataset, EchoNet Dynamic, has poor echo phase detection compared to other datasets. This suggests that the dataset has noise, which is removed in this study using three preprocessing steps: Noise reduction using heart rate formulation, Video frame synchronization, and Non-oscillating Mean absolute frame difference (MAFD). Additionally, this study formulates the echo phase detection problem as frame-level binary classification, distinguishing between diastole and systole phases. The proposed architecture takes the echo sequence as an input and a customized time-distributed CNN extracts spatial features from each frame. These frame features are fed into a bidirectional long short-term memory (BLSTM) network, capturing temporal information, followed by a classification layer. The model is trained on the pre-processed EchoNet-Dynamic dataset. Compared to true labels, an average absolute frame distance (aaFD) of 1.02 and 1.04 frames is achieved for ED and ES frames, respectively. Moreover, it operates with an inference time of less than 65 ms for an input sequence consisting of 32 frames on a graphics processing unit (GPU).

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EchoPhaseFormer: A Transformer Based Echo Phase Detection and Analysis in 2D Echocardiography

Singh,

Darji,

Sarvaiya

et al. 2024

SN COMPUT. SCI.

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Deep Learning Based Cardiac Phase Detection Using Echocardiography Imaging

Cited by 5 publications

References 11 publications

An AI-based multitask framework for cardiac function assessment through echocardiograms

An AI-based multitask framework for cardiac function assessment through echocardiograms

Preprocessing and Frame Level Classification Framework for Cardiac Phase Detection in 2D Echocardiography

EchoPhaseFormer: A Transformer Based Echo Phase Detection and Analysis in 2D Echocardiography

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