Automatic myocardial infarction detection in contrast echocardiography based on polar residual network

Guo, Yanhui; Du, Guoqing; Shen, Wenqian; Du, Chunlai; He, Peina; Siuly, Siuly

doi:10.1016/j.cmpb.2020.105791

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…Following the review of the title and abstract and, upon necessity, the full text further 187 records were rejected. After the reviewing process, a total of 36 original papers [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ] and 20 conference proceedings [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ,…”

Section: Resultsmentioning

confidence: 99%

“…Among the 56 selected papers, 16 (28.6%) proposed the application of DL to US images in cardiovascular studies. In particular, these papers focused on the heart [ 43 , 45 , 48 , 54 , 64 , 73 , 83 ], atherosclerotic plaque [ 34 ], carotid [ 56 ]/abdominal [ 40 ]/femoral [ 31 , 52 , 75 ] arteries, femoral veins [ 65 , 66 ] and inferior vena cava [ 77 ].…”

Section: Resultsmentioning

confidence: 99%

“…Murine models have been adopted in four papers [ 43 , 45 , 56 , 83 ]. Park et al [ 56 ] proposed a U-Net modified network to study flow–vessel dynamics in the carotid artery; they also demonstrated that the DL model performed better than the conventional US-based flow and strain measurement techniques in assessing vascular stiffness.…”

Section: Resultsmentioning

confidence: 99%

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Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

De Rosa,

L’Abbate,

Kusmic

et al. 2023

Life

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Background and Aim: Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models. Methods: A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines. Results: Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging. Conclusion: DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

De Rosa,

L’Abbate,

Kusmic

et al. 2023

Life

View full text Add to dashboard Cite

show abstract

“…To fairly assess the performance of the proposed models, we computed all standard measurement parameters including the accuracy, sensitivity, specificity, positive predictive value, false alarm rate, F1-score, and the receiver operating characteristic curve (ROC) in this study. e descriptions of the mentioned measurements are available in [28,[31][32][33][34][35].…”

Section: Performance Evaluation Parametersmentioning

confidence: 99%

SchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia

Siuly

Wen

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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Schizophrenia (SZ) is a severe and prolonged disorder of the human brain where people interpret reality in an abnormal way. Traditional methods of SZ detection are based on handcrafted feature extraction methods (manual process), which are tedious and unsophisticated, and also limited in their ability to balance efficiency and accuracy. To solve this issue, this study designed a deep learning-based feature extraction scheme involving the GoogLeNet model called “SchizoGoogLeNet” that can efficiently and automatically distinguish schizophrenic patients from healthy control (HC) subjects using electroencephalogram (EEG) signals with improved performance. The proposed framework involves multiple stages of EEG data processing. First, this study employs the average filtering method to remove noise and artifacts from the raw EEG signals to improve the signal-to-noise ratio. After that, a GoogLeNet model is designed to discover significant hidden features from denoised signals to identify schizophrenic patients from HC subjects. Finally, the obtained deep feature set is evaluated by the GoogleNet classifier and also some renowned machine learning classifiers to find a sustainable classification method for the obtained deep feature set. Experimental results show that the proposed deep feature extraction model with a support vector machine performs the best, producing a 99.02% correct classification rate for SZ, with an overall accuracy of 98.84%. Furthermore, our proposed model outperforms other existing methods. The proposed design is able to accurately discriminate SZ from HC, and it will be useful for developing a diagnostic tool for SZ detection.

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“…Myocardial contrast echocardiography (MCE) has been used in MI diagnosis but is time-consuming, subjective, and highly operator-dependent. In (Guo et al 2021), a new DL network named polar residual network (PResNet), based on ResNet, was proposed for automated computeraided MI diagnosis based on MCE images. The authors defined a new polar layer in the structure of PResNet that mapped subsections of MCE to the polar map.…”

Section: Deep Learningmentioning

confidence: 99%

Application of artificial intelligence techniques for automated detection of myocardial infarction: a review

Joloudari¹,

Mojrian²,

Nodehi³

et al. 2022

Physiol. Meas.

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Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals.

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Automatic myocardial infarction detection in contrast echocardiography based on polar residual network

Cited by 12 publications

References 8 publications

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

SchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia

Application of artificial intelligence techniques for automated detection of myocardial infarction: a review

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