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152. Approach to non-compressive back pain

Author

Gupta, Latika, Zanwar, Abhishek, Misra, Durga, and Agarwal, Vikas

Subjects
Back pain -- Prognosis, Carisoprodol, Litigation, Social class, Physicians, Etiology (Medicine), Technology, Working class, Public health, Health, World Health Organization
Abstract

Byline: Latika. Gupta, Abhishek. Zanwar, Durga. Misra, Vikas. Agarwal Back pain is the most common manifestation among rheumatologic conditions, with an 80% lifetime risk of development in each individual. Most [...]

Published
2019

153. ChatGPT, artificial intelligence and scientific writing: What authors, peer reviewers and editors should know.

Author

Misra, Durga Prasanna and Chandwar, Kunal

Subjects
CHATGPT, ARTIFICIAL intelligence, TECHNICAL writing, PEER review of students, JOURNAL writing, BIBLIOGRAPHIC databases, LANGUAGE models, SOCIAL media in education
Abstract

Considerations for peer reviewers and editors Peer reviewers and editors per se might not be able to distinguish human-generated text accurately from AI-generated text.[14] A few tools to detect AI-generated content are already available. It has been proposed that dialogic AI such as ChatGPT might help to improve patient care and patient education alike and revolutionise the way medicine is practiced.[5],[6] The use of AI tools to improve scientific writing is not new. Keywords: artificial intelligence; ChatGPT; peer review; scientific publication; information science; publications EN artificial intelligence ChatGPT peer review scientific publication information science publications 90 93 4 07/18/23 20230601 NES 230601 The fear of whether artificial intelligence (AI) might substitute for or replace humans is age-old. [Extracted from the article]

Published
2023
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158. Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Author

Suri, Jasjit S., primary, Maindarkar, Mahesh A., additional, Paul, Sudip, additional, Ahluwalia, Puneet, additional, Bhagawati, Mrinalini, additional, Saba, Luca, additional, Faa, Gavino, additional, Saxena, Sanjay, additional, Singh, Inder M., additional, Chadha, Paramjit S., additional, Turk, Monika, additional, Johri, Amer, additional, Khanna, Narendra N., additional, Viskovic, Klaudija, additional, Mavrogeni, Sofia, additional, Laird, John R., additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanase D., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Kolluri, Raghu, additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Sockalingam, Meyypan, additional, Saxena, Ajit, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Padukode R., additional, Omerzu, Tomaz, additional, Naidu, Subbaram, additional, Nicolaides, Andrew, additional, Paraskevas, Kosmas I., additional, Kalra, Mannudeep, additional, Ruzsa, Zoltán, additional, and Fouda, Mostafa M., additional

Published
2022
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159. COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Author

Suri, Jasjit S., primary, Agarwal, Sushant, additional, Chabert, Gian Luca, additional, Carriero, Alessandro, additional, Paschè, Alessio, additional, Danna, Pietro S. C., additional, Saba, Luca, additional, Mehmedović, Armin, additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Fouda, Mostafa M., additional, Naidu, Subbaram, additional, Viskovic, Klaudija, additional, and Kalra, Mannudeep K., additional

Published
2022
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161. COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans

Author

Suri, Jasjit S., primary, Agarwal, Sushant, additional, Chabert, Gian Luca, additional, Carriero, Alessandro, additional, Paschè, Alessio, additional, Danna, Pietro S. C., additional, Saba, Luca, additional, Mehmedović, Armin, additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Fouda, Mostafa M., additional, Naidu, Subbaram, additional, Viskovic, Klaudija, additional, and Kalra, Manudeep K., additional

Published
2022
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162. Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review

Author

Munjral, Smiksha, primary, Maindarkar, Mahesh, additional, Ahluwalia, Puneet, additional, Puvvula, Anudeep, additional, Jamthikar, Ankush, additional, Jujaray, Tanay, additional, Suri, Neha, additional, Paul, Sudip, additional, Pathak, Rajesh, additional, Saba, Luca, additional, Chalakkal, Renoh Johnson, additional, Gupta, Suneet, additional, Faa, Gavino, additional, Singh, Inder M., additional, Chadha, Paramjit S., additional, Turk, Monika, additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Viskovic, Klaudija, additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios, additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Kolluri, Raghu, additional, Teji, Jagjit, additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Sockalingam, Meyypan, additional, Saxena, Ajit, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Viswanathan, Vijay, additional, Krishnan, Padukode R., additional, Omerzu, Tomaz, additional, Naidu, Subbaram, additional, Nicolaides, Andrew, additional, Fouda, Mostafa M., additional, and Suri, Jasjit S., additional

Published
2022
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163. Response Guided Slow Infusion of Albumin, Vasoconstrictors and Furosemide Improves Ascites Mobilization and Survival in Acute on Chronic Liver Failure: A Proof-of-Concept Study

Author

Pande,Gaurav, Hatti,Manjunath, Rai,Mohit Kumar, Rai,Praveer, Kumar,Kamlesh, VP,Krishna, Nehra,Abhimanyu, Kumar,Sudeep, Ranjan Rout,Smarak, Mishra,Sourav Kumar, Kumar,Dinesh, Kumar,Umesh, Mishra,Prabhaker, Majeed,Abdul, Saraswat,Vivek Anand, Singh,Kritika, Singh,Harshit, Misra,Durga Prasanna, Agarwal,Vikas, Pande,Gaurav, Hatti,Manjunath, Rai,Mohit Kumar, Rai,Praveer, Kumar,Kamlesh, VP,Krishna, Nehra,Abhimanyu, Kumar,Sudeep, Ranjan Rout,Smarak, Mishra,Sourav Kumar, Kumar,Dinesh, Kumar,Umesh, Mishra,Prabhaker, Majeed,Abdul, Saraswat,Vivek Anand, Singh,Kritika, Singh,Harshit, Misra,Durga Prasanna, and Agarwal,Vikas

Abstract

Gaurav Pande,1 Manjunath Hatti,1 Mohit Kumar Rai,2 Praveer Rai,1 Kamlesh Kumar,1 Krishna VP,1 Abhimanyu Nehra,1 Sudeep Kumar,3 Smarak Ranjan Rout,3 Sourav Kumar Mishra,3 Dinesh Kumar,4 Umesh Kumar,4 Prabhaker Mishra,5 Abdul Majeed,1 Vivek Anand Saraswat,1,* Kritika Singh,2 Harshit Singh,2 Durga Prasanna Misra,2 Vikas Agarwal2,* 1Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India; 2Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India; 3Cardiology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India; 4Department of Advanced Spectroscopy and Imaging, Center of Biomedical Research, Lucknow, India; 5Biostatistics, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India*These authors contributed equally to this workCorrespondence: Vikas Agarwal, Unit III, Clinical Immunology and Rheumatology, SGPGIMS, Raebareli Road, Lucknow, India, Tel +918004904390, Fax +91522268812, Email vikasagr@yahoo.comBackground and Aims: Acute-on-chronic liver failure (ACLF) with increasing organ failure is associated with poor outcomes. Severely deranged systemic hemodynamics and decreased effective arterial blood volume contribute to tissue damage and organ failure. Response-guided therapy with albumin, vasoconstrictors, and furosemide may help overcome effective hypovolemia, improve diuresis and impact survival.Methods: In the observation cohort, 230 patients with ACLF (CANONIC criteria) with ascites (≥Grade II) and ACLF ≥Grade I were enrolled. A total of 136 patients (GROUP I) received response-guided (urine sodium > 80mmol/day) slow albumin-furosemide infusion ±â€…terlipressin (SAFI ± T), while 94 patients (GROUP II) received standard medical therapy. Twenty-eight-day survival, ascites mobilization (nil or grade 1), and adverse events were noted. In another mechanistic cohort (n = 40), laboratory evi

Published
2022

164. Novel Th17 Lymphocyte Populations, Th17.1 and PD1+Th17, are Increased in Takayasu Arteritis, and Both Th17 and Th17.1 Sub-Populations Associate with Active Disease

Author

Singh,Kritika, Rathore,Upendra, Rai,Mohit Kumar, Behera,Manas Ranjan, Jain,Neeraj, Ora,Manish, Bhadauria,Dharmendra, Sharma,Supriya, Pande,Gaurav, Gambhir,Sanjay, Nath,Alok, Kumar,Sudeep, Sharma,Aman, Agarwal,Vikas, Misra,Durga Prasanna, Singh,Kritika, Rathore,Upendra, Rai,Mohit Kumar, Behera,Manas Ranjan, Jain,Neeraj, Ora,Manish, Bhadauria,Dharmendra, Sharma,Supriya, Pande,Gaurav, Gambhir,Sanjay, Nath,Alok, Kumar,Sudeep, Sharma,Aman, Agarwal,Vikas, and Misra,Durga Prasanna

Abstract

Kritika Singh,1,* Upendra Rathore,1,* Mohit Kumar Rai,1 Manas R Behera,2 Neeraj Jain,3 Manish Ora,4 Dharmendra Bhadauria,2 Supriya Sharma,5 Gaurav Pande,6 Sanjay Gambhir,4 Alok Nath,7 Sudeep Kumar,8 Aman Sharma,9 Vikas Agarwal,1 Durga Prasanna Misra1 1Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, 226014, India; 2Department of Nephrology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, 226014, India; 3Department of Radiodiagnosis, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, 226014, India; 4Department of Nuclear Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, 226014, India; 5Department of Surgical Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, 226014, India; 6Department of Medical Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, 226014, India; 7Department of Pulmonary Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, 226014, India; 8Department of Cardiology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, Uttar Pradesh, 226014, India; 9Clinical Immunology and Rheumatology Services, Department of Internal Medicine, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India*These authors contributed equally to this workCorrespondence: Durga Prasanna Misra, Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, 226014, India, Tel +91 5222495273, Fax + 91 522-2668812, Email DurgapMisra@gmail.comPurpose: We evaluated T helper lymphocyte profile, including novel Th17 subsets Th17.1 (secrete IFN-γ, associate with corti

Published
2022

166. Novel Th17 Lymphocyte Populations, Th17.1 and PD1+Th17, are Increased in Takayasu Arteritis, and Both Th17 and Th17.1 Sub-Populations Associate with Active Disease

Author

Singh, Kritika, primary, Rathore, Upendra, additional, Rai, Mohit Kumar, additional, Behera, Manas Ranjan, additional, Jain, Neeraj, additional, Ora, Manish, additional, Bhadauria, Dharmendra, additional, Sharma, Supriya, additional, Pande, Gaurav, additional, Gambhir, Sanjay, additional, Nath, Alok, additional, Kumar, Sudeep, additional, Sharma, Aman, additional, Agarwal, Vikas, additional, and Misra, Durga Prasanna, additional

Published
2022
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168. A systematic review and meta-analysis of mycobacterial infections in patients with idiopathic inflammatory myopathies

Author

Haldule, Saloni, primary, Chatterjee, Moumita, additional, Goswami, Rudra Prosad, additional, Vadsaria, Innara, additional, Gaur, Prithvi, additional, Kavadichanda, Chengappa, additional, Misra, Durga Prasanna, additional, Chinoy, Hector, additional, Agarwal, Vikas, additional, Aggarwal, Rohit, additional, and Gupta, Latika, additional

Published
2022
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170. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

Author

Suri, Jasjit S., primary, Agarwal, Sushant, additional, Carriero, Alessandro, additional, Paschè, Alessio, additional, Danna, Pietro S. C., additional, Columbu, Marta, additional, Saba, Luca, additional, Viskovic, Klaudija, additional, Mehmedović, Armin, additional, Agarwal, Samriddhi, additional, Gupta, Lakshya, additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios, additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Gupta, Archna, additional, Naidu, Subbaram, additional, Paraskevas, Kosmas I., additional, and Kalra, Mannudeep K., additional

Published
2021
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173. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Author

Suri, Jasjit, primary, Agarwal, Sushant, additional, Elavarthi, Pranav, additional, Pathak, Rajesh, additional, Ketireddy, Vedmanvitha, additional, Columbu, Marta, additional, Saba, Luca, additional, Gupta, Suneet, additional, Faa, Gavino, additional, Singh, Inder, additional, Turk, Monika, additional, Chadha, Paramjit, additional, Johri, Amer, additional, Khanna, Narendra, additional, Viskovic, Klaudija, additional, Mavrogeni, Sophie, additional, Laird, John, additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David, additional, Balestrieri, Antonella, additional, Sfikakis, Petros, additional, Tsoulfas, George, additional, Protogerou, Athanasios, additional, Misra, Durga, additional, Agarwal, Vikas, additional, Kitas, George, additional, Teji, Jagjit, additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder, additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode, additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Gupta, Archna, additional, Naidu, Subbaram, additional, and Kalra, Mannudeep, additional

Published
2021
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176. Rheumatoid Hand

Author

Misra, Durga Prasanna, Parida, Jyoti Ranjan, Chowdhury, Abhra Chandra, and Agarwal, Vikas

Published
2015
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178. Virtual Consulting in the Times of COVID-19.

Author

Gupta, Latika, Sharma, Supriya, Kharbanda, Rajat, Aggarwal, Shelley, Misra, Durga Prasanna, and Agarwal, Vikas

Abstract

Background: Confinement and economic uncertainty, along with disease-specific issues such as pain and suffering may uniquely impact rheumatology patients, putting non-COVID morbidity at par with COVID-related losses. Methods: We conducted a review of records to explore the clinical profile and interventions in rheumatology patients at a tertiary care center in Northern India to identify the potential problems and propose direction for the future. Results: In our experience spanning 5 weeks, among 508 rheumatology patients who sought teleconsultations, rheumatoid arthritis was the most common diagnosis (35.2%), and 43% were asked to continue the same treatment over tele consults, whereas investigations were awaited for another 16%. Nearly one-third (29.9%) required an intervention, though this largely consisted of dose titrations (104 of 152, 68.4%). Certain diseases such as myositis were under-represented among consults (1.2%) but over-represented (33.3%) among admissions. Among the 54 patients who were asked to rate their teleconsultation experience, the voice and consultation quality were rated and average of 9.9 each, and 88.9% (48/54) said they would prefer teleconsultations until the pandemic is over. Conclusion: A sizeable proportion of rheumatology patients can be managed with teleconsultations, with some requiring greater assistance, calling for a triage protocol for the times ahead. With propositions for a deliberate transition into the realm of virtual consulting, chronic caregivers can hope to reach an optimal balance between e-consults and in-person visits. [ABSTRACT FROM AUTHOR]

Published
2022
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187. COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Author

Suri, Jasjit S., primary, Agarwal, Sushant, additional, Pathak, Rajesh, additional, Ketireddy, Vedmanvitha, additional, Columbu, Marta, additional, Saba, Luca, additional, Gupta, Suneet K., additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Viskovic, Klaudija, additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios, additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Frence, Nagy, additional, Ruzsa, Zoltan, additional, Gupta, Archna, additional, Naidu, Subbaram, additional, and Kalra, Mannudeep, additional

Published
2021
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188. Editorial: Innate immunity in vasculitis.

Author

Silva De Souza, Alexandre Wagner, Ooi, Joshua Daniel, Misra, Durga Prasanna, Yong Zhong, and Brown, Kelly L.

Subjects
NATURAL immunity, MUCOCUTANEOUS lymph node syndrome, VASCULITIS, ANTINEUTROPHIL cytoplasmic antibodies, MEDICAL sciences, KILLER cells
Abstract

This article, titled "Editorial: Innate immunity in vasculitis," discusses the role of the innate immune system in systemic vasculitis, a group of rare disorders characterized by inflammation and damage to blood vessel walls. While autoimmune factors are commonly associated with these disorders, the innate immune system, including cells like neutrophils, monocytes, and macrophages, also play a significant role in driving inflammation and damage. The article highlights recent research on genetic susceptibility factors, the relationship between antibodies and ulcerative colitis, and the evaluation of specific genes in glomerulonephritis. The authors hope that this research will contribute to a better understanding of the pathogenesis of systemic vasculitis and encourage further investigation into the role of innate immunity in these disorders. [Extracted from the article]

Published
2024
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189. Comment on: Treatment efficacy and safety of adalimumab versus tocilizumab in patients with active and severe Takayasu arteritis.

Author

Misra, Durga Prasanna

Subjects
*EFFECT sizes (Statistics), *PATIENT safety, *COMPUTER software, *SEVERITY of illness index, *PREDNISOLONE, *PREDNISONE, *TREATMENT effectiveness, *ANTIRHEUMATIC agents, *RESEARCH bias, *DRUG efficacy, *ADALIMUMAB, *RESEARCH methodology, *TAKAYASU arteritis, *TOCILIZUMAB, *DISEASE progression
Published
2024
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190. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Author

Suri, Jasjit S. Agarwal, Sushant Elavarthi, Pranav Pathak, Rajesh Ketireddy, Vedmanvitha Columbu, Marta Saba, Luca and Gupta, Suneet K. Faa, Gavino Singh, Inder M. Turk, Monika and Chadha, Paramjit S. Johri, Amer M. Khanna, Narendra N. and Viskovic, Klaudija Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sobel, David W. Balestrieri, Antonella and Sfikakis, Petros P. Tsoulfas, George Protogerou, Athanasios and Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. and Teji, Jagjit S. Al-Maini, Mustafa Dhanjil, Surinder K. and Nicolaides, Andrew Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra Krishnan, Pudukode R. Ferenc, Nagy and Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Kalra, Mannudeep K.

Abstract

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.

Published
2021

191. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

Author

Suri, Jasjit S. Agarwal, Sushant Carriero, Alessandro and Pasche, Alessio Danna, Pietro S. C. Columbu, Marta Saba, Luca Viskovic, Klaudija Mehmedovic, Armin Agarwal, Samriddhi and Gupta, Lakshya Faa, Gavino Singh, Inder M. Turk, Monika and Chadha, Paramjit S. Johri, Amer M. Khanna, Narendra N. and Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin and Sobel, David W. Balestrieri, Antonella Sfikakis, Petros P. and Tsoulfas, George Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. Teji, Jagjit S. and Al-Maini, Mustafa Dhanjil, Surinder K. Nicolaides, Andrew and Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra and Krishnan, Pudukode R. Nagy, Ferenc Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Paraskevas, Kosmas I. Kalra, Mannudeep K.

Abstract

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint(TM) Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland-Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of

Published
2021

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

Author

Saba, Luca Sanagala, Skandha S. Gupta, Suneet K. Koppula, Vijaya K. Johri, Amer M. Khanna, Narendra N. Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin and Sfikakis, Petros P. Protogerou, Athanasios Misra, Durga P. and Agarwal, Vikas Sharma, Aditya M. Viswanathan, Vijay Rathore, Vijay S. Turk, Monika Kolluri, Raghu Viskovic, Klaudija and Cuadrado-Godia, Elisa Kitas, George D. Sharma, Neeraj and Nicolaides, Andrew Suri, Jasjit S.

Abstract

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 in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Published
2021

193. COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Author

Suri, Jasjit S. Agarwal, Sushant Pathak, Rajesh Ketireddy, Vedmanvitha Columbu, Marta Saba, Luca Gupta, Suneet K. and Faa, Gavino Singh, Inder M. Turk, Monika Chadha, Paramjit S. and Johri, Amer M. Khanna, Narendra N. Viskovic, Klaudija and Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin and Sobel, David W. Balestrieri, Antonella Sfikakis, Petros P. and Tsoulfas, George Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. Teji, Jagjit S. and Al-Maini, Mustafa Dhanjil, Surinder K. Nicolaides, Andrew and Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra and Krishnan, Pudukode R. Frence, Nagy Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Kalra, Mannudeep

Abstract

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint (TM), Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of similar to 0.96, similar to 0.97, similar to 0.98, and similar to 0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in

Published
2021

196. An international audit of the management of dyslipidaemia and hypertension in patients with rheumatoid arthritis: results from 19 countries

Author

Rollefstad, Silvia, primary, Ikdahl, Eirik, additional, Wibetoe, Grunde, additional, Sexton, Joe, additional, Crowson, Cynthia S, additional, van Riel, Piet, additional, Kitas, George D, additional, Graham, Ian, additional, Dahlqvist, Solbritt Rantapää, additional, Karpouzas, George, additional, Myasoedova, Elena, additional, Gonzalez-Gay, Miguel A, additional, Sfikakis, Petros P, additional, Tektonidou, Maria G, additional, Lazarini, Argyro, additional, Vassilopoulos, Dimitrios, additional, Kuriya, Bindee, additional, Hitchon, Carol A, additional, Stoenoiu, Maria Simona, additional, Durez, Patrick, additional, Pascual-Ramos, Virginia, additional, Galarza-Delgado, Dionicio Angel, additional, Faggiano, Pompilio, additional, Misra, Durga Prasanna, additional, Borg, Andrew, additional, Mu, Rong, additional, Mirrakhimov, Erkin M, additional, Gheta, Diane, additional, Myasoedova, Svetlana, additional, Krougly, Lev, additional, Popkova, Tatiana, additional, Tuchyňová, Alena, additional, Tomcik, Michal, additional, Vrablik, Michal, additional, Lastuvka, Jiri, additional, Horák, Pavel, additional, Medková, Helena, additional, and Semb, Anne Grete, additional

Published
2021
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197. Diabetes mellitus and cardiovascular risk management in patients with rheumatoid arthritis: an international audit

Author

Semb, Anne Grete, primary, Rollefstad, Silvia, additional, Ikdahl, Eirik, additional, Wibetoe, Grunde, additional, Sexton, Joseph, additional, Crowson, Cindy, additional, van Riel, Piet, additional, Kitas, George, additional, Graham, Ian, additional, Rantapää-Dahlqvist, Solbritt, additional, Karpouzas, George Athanasios, additional, Myasoedova, Elena, additional, Gonzalez-Gay, Miguel A, additional, Sfikakis, Petros P, additional, Tektonidou, Maria G, additional, Lazarini, Argyro, additional, Vassilopoulos, Dimitrios, additional, Kuriya, Bindee, additional, Hitchon, Carol, additional, Stoenoiu, Maria Simona, additional, Durez, Patrick, additional, Pascual-Ramos, Virginia, additional, Galarza-Delgado, Dionicio Angel, additional, Faggiano, Pompilio, additional, Misra, Durga Prasanna, additional, Borg, Andrew A, additional, Mu, Rong, additional, Mirrakhimov, Erkin M, additional, Gheta, Diane, additional, Douglas, Karen, additional, Agarwal, Vikas, additional, Myasoedova, Svetlana, additional, Krougly, Lev, additional, Valentinovna Popkova, Tatiana, additional, Tuchyňová, Alena, additional, Tomcik, Michal, additional, Vrablik, Michal, additional, Lastuvka, Jiri, additional, Horak, Pavel, additional, Medkova, Helena Kaspar, additional, and Kerola, Anne M, additional

Published
2021
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198. Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Author

Saba, Luca, primary, Sanagala, Skandha S., additional, Gupta, Suneet K., additional, Koppula, Vijaya K., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sfikakis, Petros P., additional, Protogerou, Athanasios, additional, Misra, Durga P., additional, Agarwal, Vikas, additional, Sharma, Aditya M., additional, Viswanathan, Vijay, additional, Rathore, Vijay S., additional, Turk, Monika, additional, Kolluri, Raghu, additional, Viskovic, Klaudija, additional, Cuadrado-Godia, Elisa, additional, Kitas, George D., additional, Sharma, Neeraj, additional, Nicolaides, Andrew, additional, and Suri, Jasjit S., additional

Published
2021
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