Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning

Topaloğlu, Nurettin; Kazci, Omer; Koşar, Pınar Nercis

doi:10.1007/s10916-019-1406-2

Cited by 39 publications

(23 citation statements)

References 40 publications

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“…ere was also a study on carotid artery ultrasound image plaque recognition using deep learning, where FasterRCNN based on VGG16 and ResNet101 and YOLOv3 based on Darknet were used. e results found that Faster RCNN was fast and accurate in plaque recognition of ultrasound images for carotid artery and should be suggested in clinic [21]. Gao et al [22] applied the migration learning model of learning using privileged information (LUPI) in CNN to correct the intermediate state of network learning and also proposed a data generation strategy to maintain training samples; and the causal relationship with the privileged information in the label improved the insufficient medical data.…”

Section: Discussionmentioning

confidence: 99%

Prediction and Value of Ultrasound Image in Diagnosis of Fetal Central Nervous System Malformation under Deep Learning Algorithm

Zhou

2021

Scientific Programming

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This study was to explore the application of deep learning neural network (DLNN) algorithms to identify and optimize the ultrasound image so as to analyze the effect and value in diagnosis of fetal central nervous system malformation (CNSM). 63 pregnant women who were gated in the hospital were suspected of being fetal CNSM and were selected as the research objects. The ultrasound images were reserved in duplicate, and one group was defined as the control group without any processing, and images in the experimental group were processed with the convolutional neural network (CNN) algorithm to identify and optimize. The ultrasound examination results and the pathological test results before, during, and after the pregnancy were observed and compared. The results showed that the test results in the experimental group were closer to the postpartum ultrasound and the results of the pathological result, but the results in both groups showed no statistical difference in contrast to the postpartum results in terms of similarity ( P > 0.05 ). In the same pregnancy stage, the ultrasound examination results of the experimental group were higher than those in the control group, and the contrast was statistically significant ( P < 0.05 ); in the different pregnancy stages, the ultrasound examination results in the second trimester were more close to the postpartum examination results, showing statistically obvious difference ( P < 0.05 ). In conclusion, ultrasonic image based on deep learning was higher in CNSM inspection; and ultrasonic technology had to be improved for the examination in different pregnancy stages, and the accuracy of the examination results is improved. However, the amount of data in this study was too small, so the representative was not high enough, which would be improved.

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Section: Discussionmentioning

confidence: 99%

Prediction and Value of Ultrasound Image in Diagnosis of Fetal Central Nervous System Malformation under Deep Learning Algorithm

Zhou

2021

Scientific Programming

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“…The results of cross-validation experiments demonstrated a correlation of approximately 0.90 with the clinical assessment for the estimation of the lipid core, fibrous cap, and calcified tissue areas [105]. A deep learning model was developed for the classification of the carotid intima-media thickness to enable reliable early detection of atherosclerosis [106]. Araki et al introduced an automated segmentation system for both the near and far walls of the carotid artery using grayscale US morphology of the plaque for stroke risk assessment [107].…”

Section: Angiologymentioning

confidence: 99%

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Komatsu

Sakai

Dozen³

et al. 2021

Biomedicines

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Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.

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“…Figure 10C represents a conventional convolution neural other researchers have used DL to investigate the chest (29,171), coronary (172), liver (28), IMT wall (173,174) of patients, as well as lumen characterization ( 26) and carotid risk measurement in diabetic patients (30,56) and Rheumatoid arthritis (175) in arthritic patients in particular.…”

Section: Deep Learning Strategies Using Mri Ct and The Usmentioning

confidence: 99%

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Saba¹,

Skandha²,

Gupta³

et al. 2021

Ann Transl Med

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Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type

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Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning

Cited by 39 publications

References 40 publications

Prediction and Value of Ultrasound Image in Diagnosis of Fetal Central Nervous System Malformation under Deep Learning Algorithm

Prediction and Value of Ultrasound Image in Diagnosis of Fetal Central Nervous System Malformation under Deep Learning Algorithm

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

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