Citations 2
Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review
Munjral, Smiksha , Ahluwalia, Puneet , Saba, Luca , Singh, Inder M. , Chadha, Paramjit S., Turk, Monika, Johri, Amer M. , Khanna, Narendra N. , Višković, Klaudija , Laird, John R , Pareek, Gyan , Miner, Martin , Sobel, David , Balestrieri, Antonella , Sfikakis, Petros P. , Tsoulfas, George, Protogerou, Athanasios, Misra, Prasanna, Agarwal, Vikas , Teji, Jagjit S. , Al-Maini, Mustafa , Dhanjil, Surinder, Sockalingam, Meyypan, Saxena, Ajit Kumar , Rathore, Vijay , Fatemi, Mostafa , Viswanathan, Vijay , Krishnan, Pudukode, Omerzu, Tomaž , Naidu, Subbaram, Suri, Jasjit S. , Fatemi, Mostafa , Agarwal, Vikas , Munjral, Smiksha , Jamthikar, Ankush D. , Puvvula, Anudeep , Faa, Gavino , Mavrogeni, Sophie , Protogerou, Athanasios, Kitas, George D. , Kolluri, Raghu , Sharma, Aditya , Alizad, Azra , Krishnan, Pudukode R., Nicolaides, Andrew , Suri, Jasjit S.
Frontiers in Bioscience-Landmark | December 1, 2021 | Volume: 26 | Issue: 11 | Pages: No page available
Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.
Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography
Suri, Jasjit S. , Agarwal, Sushant , Elavarthi, Pranav, Ketireddy, Vedmanvitha, Columbu, Marta, Saba, Luca , Gupta, Suneet Kumar , Faa, Gavino , Singh, Inder M. , Turk, Monika, Chadha, Paramjit S., Johri, Amer M. , Khanna, Narendra N. , Višković, Klaudija , Laird, John R. , Pareek, Gyan , Miner, Martin , Sobel, David , Balestrieri, Antonella , Sfikakis, Petros P. , Tsoulfas, George, Protogerou, Athanase D. , Misra, Durga Prasanna , Agarwal, Vikas , Teji, Jagjit S. , Al-Maini, Mustafa , Dhanjil, Surinder, Rathore, Vijay , Fatemi, Mostafa , Krishnan, Pudukode, Nagy, F. , Naidu, Subbaram, Gupta, Suneet K , Faa, Gavino , Tsoulfas, George , Protogerou, Athanasios , Agarwal, Vikas , Fatemi, Mostafa , Alizad, Azra , Ferenc, Nagy , Naidu, Subbaram , Misra, Durga Prasanna , Agarwal, Sushant , Suri, Jasjit S. , Pathak, Rajesh, Saba, Luca , Mavrogeni, Sophie , Kitas, George D. , Nicolaides, Andrew , Sharma, Aditya , Alizad, Azra , Krishnan, Pudukode R., Ruzsa, Zoltán , Gupta, Archna , Kalra, Mannudeep K.
Diagnostics | November 1, 2021 | Volume: 11 | Issue: 11 | Pages: 2025
Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.