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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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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4
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Gao J, Xu L, Wan M. Incremental learning for an evolving stream of medical ultrasound images via counterfactual thinking. Comput Med Imaging Graph 2023; 109:102290. [PMID: 37647830 DOI: 10.1016/j.compmedimag.2023.102290] [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: 03/20/2023] [Revised: 07/27/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023]
Abstract
Despite the fact that traditional deep learning (DL) approaches provide promising accuracy and efficiency in medical ultrasound image analysis, they cannot replace the physician in making a diagnosis since the DL model is only appropriate in static application scenarios. Currently, most DL-based models are incapable of learning new tasks in the dynamic clinical environments due to the catastrophic forgetting of old tasks. To address the above problem, we propose an incremental classifier that is sequentially trained on evolving tasks for medical ultrasound images by counterfactual thinking. Specifically, the proposed model consists of a feature extractor and a classifier that can add new classes at any time during training. Toward a more discriminative model in the continual learning setting, a contrastive strategy is designed to leverage fine-grained information by generating a series of counterfactual regions. For model optimization, we design a multi-task loss made up of a knowledge distillation loss, a cross-entropy loss, and a contrasting loss. This objective jointly enjoys the merits of less forgetting, better accuracy, and fine-grained information utilization. A newly collected dataset with 52 medical ultrasound classification tasks is used to demonstrate the effectiveness of our method. The proposed approach achieves 76.59%, 11.67%, and 7.93% in terms of the average incremental accuracy, forgetting rate, and feature retention, respectively.
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Affiliation(s)
- Junling Gao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, PR China
| | - Lei Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, PR China; Xi'an Hospital of Traditional Chinese Medicine, Xi'an 710021, PR China
| | - Mingxi Wan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, PR China.
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5
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Ottakath N, Al-Maadeed S, Zughaier SM, Elharrouss O, Mohammed HH, Chowdhury MEH, Bouridane A. Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions. Diagnostics (Basel) 2023; 13:2614. [PMID: 37568976 PMCID: PMC10417708 DOI: 10.3390/diagnostics13152614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/25/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima-media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.
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Affiliation(s)
- Najmath Ottakath
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar; (S.A.-M.); (O.E.); (H.H.M.)
| | - Somaya Al-Maadeed
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar; (S.A.-M.); (O.E.); (H.H.M.)
| | | | - Omar Elharrouss
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar; (S.A.-M.); (O.E.); (H.H.M.)
| | - Hanadi Hassen Mohammed
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar; (S.A.-M.); (O.E.); (H.H.M.)
| | | | - Ahmed Bouridane
- Centre for Data Analytics and Cybersecurity, University of Sharjah, Sharjah 27272, United Arab Emirates;
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6
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Dubey AK, Chabert GL, Carriero A, Pasche A, Danna PSC, Agarwal S, Mohanty L, Sharma N, Yadav S, Jain A, Kumar A, Kalra MK, Sobel DW, Laird JR, Singh IM, Singh N, Tsoulfas G, Fouda MM, Alizad A, Kitas GD, Khanna NN, Viskovic K, Kukuljan M, Al-Maini M, El-Baz A, Saba L, Suri JS. Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework. Diagnostics (Basel) 2023; 13:diagnostics13111954. [PMID: 37296806 DOI: 10.3390/diagnostics13111954] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/22/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND AND MOTIVATION Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.
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Affiliation(s)
- Arun Kumar Dubey
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Alessandro Carriero
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Alessio Pasche
- Department of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy
| | - Pietro S C Danna
- Department of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
| | - Lopamudra Mohanty
- ABES Engineering College, Ghaziabad 201009, India
- Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
| | - Neeraj Sharma
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
| | - Sarita Yadav
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Achin Jain
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Ashish Kumar
- Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02115, USA
| | - David W Sobel
- Men's Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
| | - Inder M Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Azra Alizad
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - Melita Kukuljan
- Department of Interventional and Diagnostic Radiology, Clinical Hospital Center Rijeka, 51000 Rijeka, Croatia
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology & Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | - Ayman El-Baz
- Biomedical Engineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
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7
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Huang Q, Zhao L, Ren G, Wang X, Liu C, Wang W. NAG-Net: Nested attention-guided learning for segmentation of carotid lumen-intima interface and media-adventitia interface. Comput Biol Med 2023; 156:106718. [PMID: 36889027 DOI: 10.1016/j.compbiomed.2023.106718] [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: 01/11/2023] [Revised: 02/09/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023]
Abstract
Cardiovascular diseases (CVD), as the leading cause of death in the world, poses a serious threat to human health. The segmentation of carotid Lumen-intima interface (LII) and Media-adventitia interface (MAI) is a prerequisite for measuring intima-media thickness (IMT), which is of great significance for early screening and prevention of CVD. Despite recent advances, existing methods still fail to incorporate task-related clinical domain knowledge and require complex post-processing steps to obtain fine contours of LII and MAI. In this paper, a nested attention-guided deep learning model (named NAG-Net) is proposed for accurate segmentation of LII and MAI. The NAG-Net consists of two nested sub-networks, the Intima-Media Region Segmentation Network (IMRSN) and the LII and MAI Segmentation Network (LII-MAISN). It innovatively incorporates task-related clinical domain knowledge through the visual attention map generated by IMRSN, enabling LII-MAISN to focus more on the clinician's visual focus region under the same task during segmentation. Moreover, the segmentation results can directly obtain fine contours of LII and MAI through simple refinement without complicated post-processing steps. To further improve the feature extraction ability of the model and reduce the impact of data scarcity, the strategy of transfer learning is also adopted to apply the pretrained weights of VGG-16. In addition, a channel attention-based encoder feature fusion block (EFFB-ATT) is specially designed to achieve efficient representation of useful features extracted by two parallel encoders in LII-MAISN. Extensive experimental results have demonstrated that our proposed NAG-Net outperformed other state-of-the-art methods and achieved the highest performance on all evaluation metrics.
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Affiliation(s)
- Qinghua Huang
- School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China; School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
| | - Liangrun Zhao
- School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China; School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
| | - Guanqing Ren
- Shenzhen Delica Medical Equipment Co., Ltd, Shenzhen, 518132, Guangdong, China.
| | - Xiaoyi Wang
- Shenzhen Delica Medical Equipment Co., Ltd, Shenzhen, 518132, Guangdong, China.
| | - Chunying Liu
- Hospital of Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
| | - Wei Wang
- Sun Yat-sen University First Affiliated Hospital, Guangzhou, 510080, Guangdong, China.
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8
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Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare (Basel) 2022; 10:healthcare10122493. [PMID: 36554017 PMCID: PMC9777836 DOI: 10.3390/healthcare10122493] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [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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Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study. J Clin Med 2022; 11:jcm11226844. [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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Yuan Y, Li C, Zhang K, Hua Y, Zhang J. HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images. Diagnostics (Basel) 2022; 12:diagnostics12112852. [PMID: 36428911 PMCID: PMC9689104 DOI: 10.3390/diagnostics12112852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/09/2022] [Accepted: 11/13/2022] [Indexed: 11/19/2022] Open
Abstract
Carotid artery stenotic plaque segmentation in ultrasound images is a crucial means for the analysis of plaque components and vulnerability. However, segmentation of severe stenotic plaques remains a challenging task because of the heterogeneities of inter-plaques and intra-plaques, and obscure boundaries of plaques. In this paper, we propose an automated HRU-Net transfer learning method for segmenting carotid plaques, using the limited images. The HRU-Net is based on the U-Net encoder−decoder paradigm, and cross-domain knowledge is transferred for plaque segmentation by fine-tuning the pretrained ResNet-50. Moreover, a cropped-blood-vessel image augmentation is customized for the plaque position constraint during training only. Moreover, hybrid atrous convolutions (HACs) are designed to derive diverse long-range dependences for refined plaque segmentation that are used on high-level semantic layers to exploit the implicit discrimination features. The experiments are performed on 115 images; Firstly, the 10-fold cross-validation, using 40 images with severe stenosis plaques, shows that the proposed method outperforms some of the state-of-the-art CNN-based methods on Dice, IoU, Acc, and modified Hausdorff distance (MHD) metrics; the improvements on metrics of Dice and MHD are statistically significant (p < 0.05). Furthermore, our HRU-Net transfer learning method shows fine generalization performance on 75 new images with varying degrees of plaque stenosis, and it may be used as an alternative for automatic noisy plaque segmentation in carotid ultrasound images clinically.
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Affiliation(s)
- Yanchao Yuan
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- National Engineering Research Center of Telemedicine and Telehealth, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Hefei Innovation Research Institute, Beihang University, Hefei 230012, China
| | - Cancheng Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Hefei Innovation Research Institute, Beihang University, Hefei 230012, China
| | - Ke Zhang
- Department of Vascular Ultrasonography, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Yang Hua
- Department of Vascular Ultrasonography, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Beijing Diagnostic Center of Vascular Ultrasound, Beijing 100053, China
- Center of Vascular Ultrasonography, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing 100069, China
- Correspondence: (Y.H.); (J.Z.)
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Hefei Innovation Research Institute, Beihang University, Hefei 230012, China
- Correspondence: (Y.H.); (J.Z.)
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11
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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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12
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Far wall plaque segmentation and area measurement in common and internal carotid artery ultrasound using U-series architectures: An unseen Artificial Intelligence paradigm for stroke risk assessment. Comput Biol Med 2022; 149:106017. [DOI: 10.1016/j.compbiomed.2022.106017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/10/2022] [Accepted: 08/20/2022] [Indexed: 12/18/2022]
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13
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Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm. J Cardiovasc Dev Dis 2022; 9:jcdd9100326. [PMID: 36286278 PMCID: PMC9604424 DOI: 10.3390/jcdd9100326] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022] Open
Abstract
Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.
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Jena B, Saxena S, Nayak GK, Balestrieri A, Gupta N, Khanna NN, Laird JR, Kalra MK, Fouda MM, Saba L, Suri JS. Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework. Cancers (Basel) 2022; 14:4052. [PMID: 36011048 PMCID: PMC9406706 DOI: 10.3390/cancers14164052] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.
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Affiliation(s)
- Biswajit Jena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | - Gopal Krishna Nayak
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | | | - Neha Gupta
- Department of IT, Bharati Vidyapeeth’s College of Engineering, New Delhi 110056, India
| | - Narinder 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
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Luca Saba
- Department of Radiology, AOU, University of Cagliari, 09124 Cagliari, Italy
| | - Jasjit S. Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
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15
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Suri JS, Agarwal S, Saba L, Chabert GL, Carriero A, Paschè A, Danna P, Mehmedović A, Faa G, Jujaray T, Singh IM, Khanna NN, Laird JR, Sfikakis PP, Agarwal V, Teji JS, R Yadav R, Nagy F, Kincses ZT, Ruzsa Z, Viskovic K, Kalra MK. Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation. J Med Syst 2022; 46:62. [PMID: 35988110 PMCID: PMC9392994 DOI: 10.1007/s10916-022-01850-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/02/2022] [Indexed: 11/09/2022]
Abstract
Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, “COVLIAS 1.0-Unseen” proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations—two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.
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16
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Khanna NN, Maindarkar M, Puvvula A, Paul S, Bhagawati M, Ahluwalia P, Ruzsa Z, Sharma A, Munjral S, 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 J, Al-Maini M, Nicolaides A, Rathore V, Naidu S, Liblik K, Johri AM, Turk M, Sobel DW, Pareek G, Miner M, Viskovic K, Tsoulfas G, Protogerou AD, Mavrogeni S, Kitas GD, Fouda MM, Kalra MK, Suri JS. Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report. J Cardiovasc Dev Dis 2022; 9:jcdd9080268. [PMID: 36005433 PMCID: PMC9409845 DOI: 10.3390/jcdd9080268] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 07/30/2022] [Accepted: 08/09/2022] [Indexed: 12/15/2022] Open
Abstract
The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Mahesh 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
| | - Smiksha Munjral
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, 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
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Aman Sharma
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Jagjit 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 and University of Nicosia Medical School, 2408 Nicosia, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA 95119, USA
| | - 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
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA
| | - 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
| | - Manudeep K. Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Correspondence: ; Tel.: +1-916-749-5628
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Skandha SS, Agarwal M, Utkarsh K, Gupta SK, Koppula VK, Suri JS. A novel genetic algorithm-based approach for compression and acceleration of deep learning convolution neural network: an application in computer tomography lung cancer data. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07567-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Teji JS, Jain S, Gupta SK, Suri JS. NeoAI 1.0: Machine learning-based paradigm for prediction of neonatal and infant risk of death. Comput Biol Med 2022; 147:105639. [DOI: 10.1016/j.compbiomed.2022.105639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 05/01/2022] [Accepted: 05/01/2022] [Indexed: 11/29/2022]
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19
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Agarwal M, Agarwal S, Saba L, Chabert GL, Gupta S, Carriero A, Pasche A, Danna P, Mehmedovic A, Faa G, Shrivastava S, Jain K, Jain H, Jujaray T, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Sobel DW, Miner M, Balestrieri A, Sfikakis PP, Tsoulfas G, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Yadav RR, Nagy F, Kincses ZT, Ruzsa Z, Naidu S, Viskovic K, Kalra MK, Suri JS. Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0. Comput Biol Med 2022; 146:105571. [PMID: 35751196 PMCID: PMC9123805 DOI: 10.1016/j.compbiomed.2022.105571] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/05/2022] [Accepted: 04/26/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization. METHOD ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted. RESULTS Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions. CONCLUSIONS Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.
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Affiliation(s)
- Mohit Agarwal
- Department of Computer Science Engineering, Bennett University, India
| | - Sushant Agarwal
- Department of Computer Science Engineering, PSIT, Kanpur, India; Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Suneet Gupta
- Department of Computer Science Engineering, Bennett University, India
| | - Alessandro Carriero
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Alessio Pasche
- Depart of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | - Pietro Danna
- Depart of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | | | - Gavino Faa
- Department of Pathology - AOU of Cagliari, Italy
| | - Saurabh Shrivastava
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Kanishka Jain
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Harsh Jain
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Tanay Jujaray
- Dept of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, CA, USA
| | | | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | | | - Amer M Johri
- Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - David W Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, Thessaloniki, Greece
| | | | | | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Canada
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and Univ. of Nicosia Medical School, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | | | - Mostafa Fatemi
- Dept. of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN, USA
| | - Azra Alizad
- Dept. of Radiology, Mayo Clinic College of Medicine and Science, MN, USA
| | | | | | - Frence Nagy
- Department of Radiology, University of Szeged, 6725, Hungary
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Budapest, Hungary
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN, USA
| | | | - Manudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jasjit S Suri
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India; Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA.
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20
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COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans. Diagnostics (Basel) 2022; 12:diagnostics12061482. [PMID: 35741292 PMCID: PMC9221733 DOI: 10.3390/diagnostics12061482] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 02/07/2023] Open
Abstract
Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.
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21
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Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12051234. [PMID: 35626389 PMCID: PMC9140106 DOI: 10.3390/diagnostics12051234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/11/2022] [Accepted: 05/11/2022] [Indexed: 11/18/2022] Open
Abstract
Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.
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22
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Adam CA, Șalaru DL, Prisacariu C, Marcu DTM, Sascău RA, Stătescu C. Novel Biomarkers of Atherosclerotic Vascular Disease-Latest Insights in the Research Field. Int J Mol Sci 2022; 23:ijms23094998. [PMID: 35563387 PMCID: PMC9103799 DOI: 10.3390/ijms23094998] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 02/06/2023] Open
Abstract
The atherosclerotic vascular disease is a cardiovascular continuum in which the main role is attributed to atherosclerosis, from its appearance to its associated complications. The increasing prevalence of cardiovascular risk factors, population ageing, and burden on both the economy and the healthcare system have led to the development of new diagnostic and therapeutic strategies in the field. The better understanding or discovery of new pathophysiological mechanisms and molecules modulating various signaling pathways involved in atherosclerosis have led to the development of potential new biomarkers, with key role in early, subclinical diagnosis. The evolution of technological processes in medicine has shifted the attention of researchers from the profiling of classical risk factors to the identification of new biomarkers such as midregional pro-adrenomedullin, midkine, stromelysin-2, pentraxin 3, inflammasomes, or endothelial cell-derived extracellular vesicles. These molecules are seen as future therapeutic targets associated with decreased morbidity and mortality through early diagnosis of atherosclerotic lesions and future research directions.
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Affiliation(s)
- Cristina Andreea Adam
- Institute of Cardiovascular Diseases “Prof. Dr. George I.M. Georgescu”, 700503 Iași, Romania; (C.A.A.); (C.P.); (R.A.S.); (C.S.)
| | - Delia Lidia Șalaru
- Institute of Cardiovascular Diseases “Prof. Dr. George I.M. Georgescu”, 700503 Iași, Romania; (C.A.A.); (C.P.); (R.A.S.); (C.S.)
- Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iași, Romania;
- Correspondence:
| | - Cristina Prisacariu
- Institute of Cardiovascular Diseases “Prof. Dr. George I.M. Georgescu”, 700503 Iași, Romania; (C.A.A.); (C.P.); (R.A.S.); (C.S.)
- Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iași, Romania;
| | - Dragoș Traian Marius Marcu
- Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iași, Romania;
| | - Radu Andy Sascău
- Institute of Cardiovascular Diseases “Prof. Dr. George I.M. Georgescu”, 700503 Iași, Romania; (C.A.A.); (C.P.); (R.A.S.); (C.S.)
- Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iași, Romania;
| | - Cristian Stătescu
- Institute of Cardiovascular Diseases “Prof. Dr. George I.M. Georgescu”, 700503 Iași, Romania; (C.A.A.); (C.P.); (R.A.S.); (C.S.)
- Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iași, Romania;
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Das S, Nayak GK, Saba L, Kalra M, Suri JS, Saxena S. An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review. Comput Biol Med 2022; 143:105273. [PMID: 35228172 DOI: 10.1016/j.compbiomed.2022.105273] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/15/2022] [Accepted: 01/24/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has become a prominent technique for medical diagnosis and represents an essential role in detecting brain tumors. Although AI-based models are widely used in brain lesion segmentation (BLS), understanding their effectiveness is challenging due to their complexity and diversity. Several reviews on brain tumor segmentation are available, but none of them describe a link between the threats due to risk-of-bias (RoB) in AI and its architectures. In our review, we focused on linking RoB and different AI-based architectural Cluster in popular DL framework. Further, due to variance in these designs and input data types in medical imaging, it is necessary to present a narrative review considering all facets of BLS. APPROACH The proposed study uses a PRISMA strategy based on 75 relevant studies found by searching PubMed, Scopus, and Google Scholar. Based on the architectural evolution, DL studies were subsequently categorized into four classes: convolutional neural network (CNN)-based, encoder-decoder (ED)-based, transfer learning (TL)-based, and hybrid DL (HDL)-based architectures. These studies were then analyzed considering 32 AI attributes, with clusters including AI architecture, imaging modalities, hyper-parameters, performance evaluation metrics, and clinical evaluation. Then, after these studies were scored for all attributes, a composite score was computed, normalized, and ranked. Thereafter, a bias cutoff (AP(ai)Bias 1.0, AtheroPoint, Roseville, CA, USA) was established to detect low-, moderate- and high-bias studies. CONCLUSION The four classes of architectures, from best-to worst-performing, are TL > ED > CNN > HDL. ED-based models had the lowest AI bias for BLS. This study presents a set of three primary and six secondary recommendations for lowering the RoB.
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Affiliation(s)
- Suchismita Das
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India; CSE Department, KIIT Deemed to be University, Bhubaneswar, Odisha, India
| | - G K Nayak
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India
| | - Luca Saba
- Department of Radiology, AOU, University of Cagliari, Cagliari, Italy
| | - Mannudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA, USA.
| | - Sanjay Saxena
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India
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Suri JS, Bhagawati M, Paul S, Protogerou AD, Sfikakis PP, Kitas GD, Khanna NN, Ruzsa Z, Sharma AM, Saxena S, Faa G, Laird JR, Johri AM, Kalra MK, Paraskevas KI, Saba L. A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12030722. [PMID: 35328275 PMCID: PMC8947682 DOI: 10.3390/diagnostics12030722] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/10/2022] [Accepted: 03/13/2022] [Indexed: 12/16/2022] Open
Abstract
Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong 793022, India; (M.B.); (S.P.)
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong 793022, India; (M.B.); (S.P.)
| | - Athanasios D. Protogerou
- Research Unit Clinic, Laboratory of Pathophysiology, Department of Cardiovascular Prevention, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 11527 Athens, Greece;
| | - George D. Kitas
- Arthritis Research UK Centre for Epidemiology, Manchester University, Manchester 46962, UK;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110020, India;
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary;
| | - Aditya M. Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22903, USA;
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India;
| | - Gavino Faa
- Department of Pathology, A.O.U., di Cagliari-Polo di Monserrato s.s., 09045 Cagliari, Italy;
| | - John R. Laird
- Cardiology Department, St. Helena Hospital, St. Helena, CA 94574, USA;
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, N. Iraklio, 14122 Athens, Greece;
| | - Luca Saba
- Department of Radiology, A.O.U., di Cagliari-Polo di Monserrato s.s., 09045 Cagliari, Italy;
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Nillmani, Jain PK, Sharma N, Kalra MK, Viskovic K, Saba L, Suri JS. Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models. Diagnostics (Basel) 2022; 12:652. [PMID: 35328205 PMCID: PMC8946935 DOI: 10.3390/diagnostics12030652] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/04/2022] [Accepted: 03/04/2022] [Indexed: 12/31/2022] Open
Abstract
Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes—including COVID-19—are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks—namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152—for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.
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Affiliation(s)
- Nillmani
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India; (N.); (P.K.J.); (N.S.)
| | - Pankaj K. Jain
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India; (N.); (P.K.J.); (N.S.)
| | - Neeraj Sharma
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India; (N.); (P.K.J.); (N.S.)
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02115, USA;
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy;
| | - Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint, Roseville, CA 95661, USA
- Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
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26
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Carotid Ultrasound Boundary Study (CUBS): Technical considerations on an open multi-center analysis of computerized measurement systems for intima-media thickness measurement on common carotid artery longitudinal B-mode ultrasound scans. Comput Biol Med 2022; 144:105333. [DOI: 10.1016/j.compbiomed.2022.105333] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 02/02/2022] [Accepted: 02/16/2022] [Indexed: 01/17/2023]
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