1
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Agarwal S, Saxena S, Carriero A, Chabert GL, Ravindran G, Paul S, Laird JR, Garg D, Fatemi M, Mohanty L, Dubey AK, Singh R, Fouda MM, Singh N, Naidu S, Viskovic K, Kukuljan M, Kalra MK, Saba L, Suri JS. COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography. Front Artif Intell 2024; 7:1304483. [PMID: 39006802 PMCID: PMC11240867 DOI: 10.3389/frai.2024.1304483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 06/10/2024] [Indexed: 07/16/2024] Open
Abstract
Background and novelty When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections. Methodology Annotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland-Altman, and (iv) Correlation plots. Results Among the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann-Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001. Conclusion Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.
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Affiliation(s)
- Sushant Agarwal
- Advanced Knowledge Engineering Center, GBTI, Roseville, CA, United States
- Department of CSE, PSIT, Kanpur, India
| | | | - Alessandro Carriero
- Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), Novara, Italy
| | | | - Gobinath Ravindran
- Department of Civil Engineering, SR University, Warangal, Telangana, India
| | - Sudip Paul
- Department of Biomedical Engineering, NEHU, Shillong, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, United States
| | - Deepak Garg
- School of CS and AI, SR University, Warangal, Telangana, India
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Lopamudra Mohanty
- Department of Computer Science, ABES Engineering College, Ghaziabad, UP, India
- Department of Computer science, Bennett University, Greater Noida, UP, India
| | - Arun K. Dubey
- Bharati Vidyapeeth’s College of Engineering, New Delhi, India
| | - Rajesh Singh
- Division of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India
| | - Mostafa M. Fouda
- Department of ECE, Idaho State University, Pocatello, ID, United States
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, India
| | - Subbaram Naidu
- Department of EE, University of Minnesota, Duluth, MN, United States
| | | | - Melita Kukuljan
- Department of Interventional and Diagnostic Radiology, Clinical Hospital Center Rijeka, Rijeka, Croatia
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Luca Saba
- Department of Radiology, A.O.U., Cagliari, Italy
| | - Jasjit S. Suri
- Department of ECE, Idaho State University, Pocatello, ID, United States
- Department of Computer Science, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, India
- Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
- Stroke and Monitoring Division, AtheroPoint LLC, Roseville, CA, United States
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2
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Bhagawati M, Paul S, Mantella L, Johri AM, Laird JR, Singh IM, Singh R, Garg D, Fouda MM, Khanna NN, Cau R, Abraham A, Al-Maini M, Isenovic ER, Sharma AM, Fernandes JFE, Chaturvedi S, Karla MK, Nicolaides A, Saba L, Suri JS. Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024; 40:1283-1303. [PMID: 38678144 DOI: 10.1007/s10554-024-03100-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 04/02/2024] [Indexed: 04/29/2024]
Abstract
The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.
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Affiliation(s)
- Mrinalini Bhagawati
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Laura Mantella
- Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Canada
| | - Amer M Johri
- Division of Cardiology, Department of Medicine, Queen's University, Kingston, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Inder M Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, 95661, USA
| | - Rajesh Singh
- Division of Research and Innovation, UTI, Uttaranchal University, Dehradun, India
| | - Deepak Garg
- School of Cowereter Science and Artificial Intelligence, SR University, Warangal, Telangana, 506371, India
| | - Mostafa M Fouda
- Department of ECE, Idaho State University, Pocatello, ID, 83209, USA
| | | | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | | | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 11001, Belgrade, Serbia
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, 22904, USA
| | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Mannudeep K Karla
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia, Cyprus
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, 95661, USA.
- Department of ECE, Idaho State University, Pocatello, ID, 83209, USA.
- Department of CE, Graphic Era Deemed to be University, 248002, Dehradun, India.
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3
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Saba L, Maindarkar M, Johri AM, Mantella L, Laird JR, Khanna NN, Paraskevas KI, Ruzsa Z, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh N, Isenovic ER, Viswanathan V, Fouda MM, Suri JS. UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review. Rev Cardiovasc Med 2024; 25:184. [PMID: 39076491 PMCID: PMC11267214 DOI: 10.31083/j.rcm2505184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/24/2024] [Accepted: 03/05/2024] [Indexed: 07/31/2024] Open
Abstract
Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( aiP 3 ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP 3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP 3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP 3 model signifies a promising advancement in CVD/Stroke risk assessment.
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Affiliation(s)
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
| | - Mahesh Maindarkar
- School of Bioengineering Sciences and Research, MIT Art, Design and Technology University, 412021 Pune, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Laura Mantella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, 110001 New Delhi, India
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary
| | - Manudeep K. Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD 20742, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, 2368 Agios Dometios, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA 95823, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, 248002 Uttarakhand, India
| | - Esma R. Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | | | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, 248002 Uttarakhand, India
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4
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Karakasis P, Nasoufidou A, Sagris M, Fragakis N, Tsioufis K. Vascular Alterations Following COVID-19 Infection: A Comprehensive Literature Review. Life (Basel) 2024; 14:545. [PMID: 38792566 PMCID: PMC11122535 DOI: 10.3390/life14050545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 04/16/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024] Open
Abstract
SARS-CoV-2, the causative agent of the ongoing COVID-19 pandemic, has revealed a broader impact beyond the respiratory system, predominantly affecting the vascular system with various adverse manifestations. The infection induces endothelial dysfunction and immune system dysregulation, creating an inflammatory and hypercoagulable state. It affects both microvasculature and macrovasculature, leading to thromboembolic events, cardiovascular manifestations, impaired arterial stiffness, cerebrovascular complications, and nephropathy, as well as retinopathy-frequently observed in cases of severe illness. Evidence suggests that SARS-CoV-2 infection may result in persistent effects on the vascular system, identified as long-term COVID-19. This is characterized by prolonged inflammation, endotheliopathy, and an increased risk of vascular complications. Various imaging modalities, histopathological studies, and diagnostic tools such as video capillaroscopy and magnetic resonance imaging have been employed to visualize vascular alterations. This review aims to comprehensively summarize the evidence concerning short and long-term vascular alterations following COVID-19 infection, investigating their impact on patients' prognosis, and providing an overview of preventive strategies to mitigate associated vascular complications.
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Affiliation(s)
- Paschalis Karakasis
- Second Department of Cardiology, Aristotle University of Thessaloniki, Hippokration General Hospital, 54642 Thessaloniki, Greece; (P.K.); (A.N.); (N.F.)
| | - Athina Nasoufidou
- Second Department of Cardiology, Aristotle University of Thessaloniki, Hippokration General Hospital, 54642 Thessaloniki, Greece; (P.K.); (A.N.); (N.F.)
| | - Marios Sagris
- First Department of Cardiology, National and Kapodistrian University of Athens, Hippokration General Hospital, 11527 Athens, Greece;
| | - Nikolaos Fragakis
- Second Department of Cardiology, Aristotle University of Thessaloniki, Hippokration General Hospital, 54642 Thessaloniki, Greece; (P.K.); (A.N.); (N.F.)
| | - Konstantinos Tsioufis
- First Department of Cardiology, National and Kapodistrian University of Athens, Hippokration General Hospital, 11527 Athens, Greece;
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5
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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6
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Al-Maini M, Maindarkar M, Kitas GD, Khanna NN, Misra DP, Johri AM, Mantella L, Agarwal V, Sharma A, Singh IM, Tsoulfas G, Laird JR, Faa G, Teji J, Turk M, Viskovic K, Ruzsa Z, Mavrogeni S, Rathore V, Miner M, Kalra MK, Isenovic ER, Saba L, Fouda MM, Suri JS. Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review. Rheumatol Int 2023; 43:1965-1982. [PMID: 37648884 DOI: 10.1007/s00296-023-05415-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023]
Abstract
The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.
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Affiliation(s)
- Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada
| | - Mahesh Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Asia Pacific Vascular Society, New Delhi, 110001, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, M13 9PL, UK
| | - Narendra N Khanna
- Asia Pacific Vascular Society, New Delhi, 110001, India
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India
| | | | - Amer M Johri
- Division of Cardiology, Department of Medicine, Queen's University, Kingston, Canada
| | - Laura Mantella
- Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Canada
| | - Vikas Agarwal
- Department of Immunology, SGPIMS, Lucknow, 226014, India
| | - Aman Sharma
- Department of Immunology, SGPIMS, Lucknow, 226014, India
| | - Inder M Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124, Thessaloniki, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124, Cagliari, Italy
| | - Jagjit Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753, Delmenhorst, Germany
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, UHID, 10 000, Zagreb, Croatia
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, Athens, Greece
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA
| | - Martin Miner
- Men's Health Centre, Miriam Hospital Providence, Providence, RI, 02906, USA
| | - Manudeep K Kalra
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of the Republic of Serbia, University of Belgrade, 11000, Belgrade, Serbia
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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7
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Adam CA, Marcu DTM, Mitu O, Roca M, Aursulesei Onofrei V, Zabara ML, Tribuș LC, Cumpăt C, Crișan Dabija R, Mitu F. Old and Novel Predictors for Cardiovascular Risk in Diabetic Foot Syndrome—A Narrative Review. APPLIED SCIENCES 2023; 13:5990. [DOI: 10.3390/app13105990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Diabetic foot syndrome (DFS) is a complication associated with diabetes that has a strong negative impact, both medically and socio-economically. Recent epidemiological data show that one in six patients with diabetes will develop an ulcer in their lifetime. Vascular complications associated with diabetic foot have multiple prognostic implications in addition to limiting functional status and leading to decreased quality of life for these patients. We searched the electronic databases of PubMed, MEDLINE and EMBASE for studies that evaluated the role of DFS as a cardiovascular risk factor through the pathophysiological mechanisms involved, in particular the inflammatory ones and the associated metabolic changes. In the era of evidence-based medicine, the management of these cases in multidisciplinary teams of “cardio-diabetologists” prevents the occurrence of long-term disabling complications and has prognostic value for cardiovascular morbidity and mortality among diabetic patients. Identifying artificial-intelligence-based cardiovascular risk prediction models or conducting extensive clinical trials on gene therapy or potential therapeutic targets promoted by in vitro studies represent future research directions with a modulating role on the risk of morbidity and mortality in patients with DFS.
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Affiliation(s)
- Cristina Andreea Adam
- Department of Medical Specialties I and III and Department of Surgical Specialties, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Clinical Rehabilitation Hospital, Cardiovascular Rehabilitation Clinic, 700661 Iasi, Romania
| | - Dragos Traian Marius Marcu
- Department of Medical Specialties I and III and Department of Surgical Specialties, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Clinical Hospital of Pneumophthisiology Iași, 700115 Iasi, Romania
| | - Ovidiu Mitu
- Department of Medical Specialties I and III and Department of Surgical Specialties, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- “St. Spiridon” Clinical Emergency Hospital, 700111 Iasi, Romania
| | - Mihai Roca
- Department of Medical Specialties I and III and Department of Surgical Specialties, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Clinical Rehabilitation Hospital, Cardiovascular Rehabilitation Clinic, 700661 Iasi, Romania
| | - Viviana Aursulesei Onofrei
- Department of Medical Specialties I and III and Department of Surgical Specialties, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- “St. Spiridon” Clinical Emergency Hospital, 700111 Iasi, Romania
| | - Mihai Lucian Zabara
- Department of Medical Specialties I and III and Department of Surgical Specialties, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Laura Carina Tribuș
- Department of Internal Medicine, Faculty of Dentistry, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Internal Medicine, Ilfov County Emergency Hospital, 022104 Bucharest, Romania
| | - Carmen Cumpăt
- Department of Medical Specialties I and III and Department of Surgical Specialties, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Management, “Alexandru Ioan Cuza” University, 700506 Iasi, Romania
| | - Radu Crișan Dabija
- Department of Medical Specialties I and III and Department of Surgical Specialties, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Clinical Hospital of Pneumophthisiology Iași, 700115 Iasi, Romania
| | - Florin Mitu
- Department of Medical Specialties I and III and Department of Surgical Specialties, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Clinical Hospital of Pneumophthisiology Iași, 700115 Iasi, Romania
- Academy of Medical Sciences, 030167 Bucharest, Romania
- Academy of Romanian Scientists, 700050 Iasi, Romania
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8
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Khanna NN, Maindarkar MA, Viswanathan V, Fernandes JFE, Paul S, Bhagawati M, Ahluwalia P, Ruzsa Z, Sharma A, Kolluri R, Singh IM, Laird JR, Fatemi M, Alizad A, Saba L, Agarwal V, Sharma A, Teji JS, Al-Maini M, Rathore V, Naidu S, Liblik K, Johri AM, Turk M, Mohanty L, Sobel DW, Miner M, Viskovic K, Tsoulfas G, Protogerou AD, Kitas GD, Fouda MM, Chaturvedi S, Kalra MK, Suri JS. Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare (Basel) 2022; 10:2493. [PMID: 36554017 PMCID: PMC9777836 DOI: 10.3390/healthcare10122493] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/03/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Mahesh A. Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | | | | | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
| | - Zoltan Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Raghu Kolluri
- Ohio Health Heart and Vascular, Columbus, OH 43214, USA
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
| | - Vikas Agarwal
- Department of Immunology, SGPGIMS, Lucknow 226014, India
| | - Aman Sharma
- Department of Immunology, SGPGIMS, Lucknow 226014, India
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | | | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
| | - Kiera Liblik
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
| | - Lopamudra Mohanty
- Department of Computer Science, ABES Engineering College, Ghaziabad 201009, India
| | - David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | | | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
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9
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Khanna NN, Maindarkar MA, Viswanathan V, Puvvula A, Paul S, Bhagawati M, Ahluwalia P, Ruzsa Z, Sharma A, Kolluri R, Krishnan PR, Singh IM, Laird JR, Fatemi M, Alizad A, Dhanjil SK, Saba L, Balestrieri A, Faa G, Paraskevas KI, Misra DP, Agarwal V, Sharma A, Teji JS, Al-Maini M, Nicolaides A, Rathore V, Naidu S, Liblik K, Johri AM, Turk M, Sobel DW, Miner M, Viskovic K, Tsoulfas G, Protogerou AD, Mavrogeni S, Kitas GD, Fouda MM, Kalra MK, Suri JS. Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study. J Clin Med 2022; 11:6844. [PMID: 36431321 PMCID: PMC9693632 DOI: 10.3390/jcm11226844] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 11/22/2022] Open
Abstract
A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Mahesh A. Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | | | - Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Annu’s Hospitals for Skin and Diabetes, Nellore 524101, India
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
| | - Zoltan Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Raghu Kolluri
- Ohio Health Heart and Vascular, Columbus, OH 43214, USA
| | | | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Surinder K. Dhanjil
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
| | - Antonella Balestrieri
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124 Cagliari, Italy
| | | | | | - Vikas Agarwal
- Department of Immunology, SGPGIMS, Lucknow 226014, India
| | - Aman Sharma
- Department of Immunology, SGPGIMS, Lucknow 226014, India
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Egkomi 2408, Cyprus
| | | | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
| | - Kiera Liblik
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
| | - David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 17674 Athens, Greece
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | | | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
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10
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Kuanr M, Mohapatra P, Mittal S, Maindarkar M, Fouda MM, Saba L, Saxena S, Suri JS. Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity. Diagnostics (Basel) 2022; 12:2700. [PMID: 36359545 PMCID: PMC9689970 DOI: 10.3390/diagnostics12112700] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 10/30/2022] [Accepted: 11/03/2022] [Indexed: 09/09/2023] Open
Abstract
Background: Hospitals face a significant problem meeting patients' medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell-Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.
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Affiliation(s)
- Madhusree Kuanr
- Department of Computer Science and Engineering, IIIT, Bhubaneswar 751003, India
| | | | - Sanchi Mittal
- Department of Computer Science and Engineering, IIIT, Bhubaneswar 751003, India
| | - Mahesh Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPointTM, Roseville, CA 95661, USA
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09123 Cagliari, Italy
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIIT, Bhubaneswar 751003, India
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPointTM, Roseville, CA 95661, USA
- Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
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