Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk

Biswas, Mainak; Kuppili, Venkatanareshbabu; Saba, Luca; Edla, Damodar Reddy; Suri, Harman S.; Sharma, Aditya; Cuadrado-Godia, Elisa; Laird, John R.; Nicolaides, Andrew; Suri, Jasjit S.

doi:10.1007/s11517-018-1897-x

Cited by 63 publications

(48 citation statements)

References 38 publications

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“…Similarly, ML and DL algorithms have been applied to CVD risk assessments in several other areas [ 158 , 162 ]. Highly accurate techniques for lumen characterization [ 164 ], stenosis estimation [ 165 ], and cIMT measurement [ 56 ] have been developed using the deep fully convolutional network (FCN) [ 163 ] in segmentation models.…”

Section: Machine Learning and Deep Learning For Tissue Characterizatimentioning

confidence: 99%

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

Suri¹,

Puvvula²,

Biswas³

et al. 2020

Computers in Biology and Medicine

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102

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Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1 st , 2020 . Since the outbreak began, almost 28,000 articles about COVID-19 have been published ( https://pubmed.ncbi.nlm.nih.gov ); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients—specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.

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Section: Machine Learning and Deep Learning For Tissue Characterizatimentioning

confidence: 99%

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

Suri¹,

Puvvula²,

Biswas³

et al. 2020

Computers in Biology and Medicine

Self Cite

102

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“…Unlike ML-based algorithms, DLbased models, such as convolutional neural networks, do not require input features beforehand. Instead, such algorithms automatically learn their offline coefficients from the input image datasets [111]. Currently, AI-based techniques are used in the diagnosis of RA [57], the identification of RA disease severity [58], the classification of several RA synovial tissues [59], and mortality prediction due to RA [60].…”

Section: Artificial Intelligence In Cvd/stroke Risk Assessmentmentioning

confidence: 99%

“…Unlike ML-based algorithms, DL-based models, such as convolutional neural networks, do not require input features beforehand. Instead, such algorithms automatically learn their offline coefficients from the input image datasets [ 111 ].…”

Section: Artificial Intelligence In Cvd/stroke Risk Assessmentmentioning

confidence: 99%

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Jamthikar

Gupta

Puvvula³

et al. 2020

Rheumatol Int

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Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extraarticular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

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“…13 On the contrary, to make the ultrasound measurements with less operator dependence and better inter-operator agreement, a variety of smart algorithms have been developed for automated ultrasound measurement for different parameters, for example, vessel diameter and wall thickness. [14][15][16][17][18] Regarding the measurement of the IVC diameter using ultrasound, computer-assisted technologies have been developed for both longitudinal and transverse measurements. [19][20][21][22] Using longitudinal scanning, Mesin et al 19 proposed a semi-automated approach in which the operator was required to first place two reference markers as a base for future movement and rotation tracking, and then use two more points to indicate a moving M-line.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, most existing ultrasound automation studies were based on ultrasound images and utilized algorithms that require high computation capabilities, such as active contours and artificial neural networks. [14][15][16][17][18] There are several limitations to the study. First, although the ultrasound data were collected by experienced clinicians and under good control, the IVC image quality was affected by a number of factors including body habitus, bowel gas, ultrasound artifact, the relative depth of the IVC in the body, and other factors, for example, gain, frame rate, depth of the machine and motions, and so on.…”

mentioning

confidence: 99%

Automated Ultrasound Measurement of the Inferior Vena Cava: An Animal Study

Chen

Ding

et al. 2020

Ultrason Imaging

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Because of continuous movement and variation in diameter of the inferior vena cava (IVC) with respiration, the measurements on IVC are labor-intensive and with considerable inter-operator variations. Some computer-assisted techniques have been developed to track the movement of the IVC semi-automatically. However, existing methods predominantly rely on reference marker selection and require many manual inputs. In this study, we developed a cross-correlation (CC)-based method for automated IVC movement tracking and measurement, which requires minimal manual input and avoids manual selection of reference markers. Based on the CC method, two approaches, named direct and relative approaches, were used to calculate the maximum, minimum, and variation of the IVC diameter, and compared with the manual measurement. Fifty-four ultrasound cine-loops collected from nine pigs were tested. The results reveal that both the proposed approaches were well agreed with the manual measurement. The errors of the direct approach were less than 9%, while those of relative approach were as high as 26.7%. It is concluded that the proposed direct approach is superior for IVC diameter measurements, which can be comparable with manual counterpart, serving as an alternative to traditional IVC measurement.

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Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk

Cited by 63 publications

References 38 publications

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Automated Ultrasound Measurement of the Inferior Vena Cava: An Animal Study

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