Computers in Biology and Medicine

2020

DOI: 10.1016/j.compbiomed.2020.103847

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Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment

Mainak Biswas¹,

²

,

Shubhro Chakrabartty

³

et al.

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Deep Learning Strategies Using Mri Ct and The Us1

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Non-invasive Cvd Risk Assessment Beyond "Current Covid-19 Time"1

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Cited by 55 publications

(38 citation statements)

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“…All the above studies were focused on wall segmentation and cIMT measurement. Recently, AI tools were developed for the joint estimation of wall thickness and area estimation by Biswas et al [ 10 ]. Molinari et al used five ethnic databases for IMT measurement using automated and semi-automated methods [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

“…The plaque formation worsens with comorbidities such as diabetes [ 5 ], hypertension [ 6 ], renal disease [ 7 ], and heart disease [ 8 ]. Thus, it is vital to detect wall plaque during the early stages of its formation using angiography screening techniques [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unseen Artificial Intelligence—Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

¹

,

²

,

³

et al. 2021

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Background: The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the “same” ethnic group (“Seen AI”). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the “Unseen AI” paradigm where training and testing are from “different” ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between “Unseen AI” and “Seen AI”. Methodology: Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. “Unseen AI” (training: Japanese, testing: HK or vice versa) and “Seen AI” experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability. Results: When using the UNet DL architecture, the “Unseen AI” pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for “Unseen AI” pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using “Seen AI”, the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively. Conclusion: We demonstrated that “Unseen AI” was in close proximity (<10%) to “Seen AI”, validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s.

“…All the above studies were focused on wall segmentation and cIMT measurement. Recently, AI tools were developed for the joint estimation of wall thickness and area estimation by Biswas et al [ 10 ]. Molinari et al used five ethnic databases for IMT measurement using automated and semi-automated methods [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

“…The plaque formation worsens with comorbidities such as diabetes [ 5 ], hypertension [ 6 ], renal disease [ 7 ], and heart disease [ 8 ]. Thus, it is vital to detect wall plaque during the early stages of its formation using angiography screening techniques [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Unseen Artificial Intelligence—Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

¹

,

²

,

³

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background: The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the “same” ethnic group (“Seen AI”). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the “Unseen AI” paradigm where training and testing are from “different” ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between “Unseen AI” and “Seen AI”. Methodology: Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. “Unseen AI” (training: Japanese, testing: HK or vice versa) and “Seen AI” experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability. Results: When using the UNet DL architecture, the “Unseen AI” pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for “Unseen AI” pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using “Seen AI”, the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively. Conclusion: We demonstrated that “Unseen AI” was in close proximity (<10%) to “Seen AI”, validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s.

“…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,

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

“…Experiments on IMT segmentation were not limited to non-AI only, where authors in [ 21 ] implemented a screening tool that integrates a two-stage artificial intelligence model for IMT and carotid plaque measurements, which consists of a CNN and fully convolutional network (FCN). The system goes through two deep learning models, as the first divides the CCA from the ultrasound images into two categories the rectangular wall and non-wall patches.…”

Section: Related Workmentioning

confidence: 99%

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

¹

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²

,

³

et al. 2021

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Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.

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