1
|
Bhadra R, Singh PK, Mahmud M. HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals. Brain Inform 2024; 11:21. [PMID: 39167115 PMCID: PMC11339197 DOI: 10.1186/s40708-024-00234-x] [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: 05/23/2024] [Accepted: 07/24/2024] [Indexed: 08/23/2024] Open
Abstract
Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES detection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and prevention of further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on those extracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeley datasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain.
Collapse
Affiliation(s)
- Rajdeep Bhadra
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, 700 106, Kolkata, West Bengal, India
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, 700 106, Kolkata, West Bengal, India
- Metharath University, 99, Moo 10, Bang Toei, Sam Khok, 12160, Pathum Thani, Thailand
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Nottingham, NG11 8NS, UK.
- Medical Technologies Innovation Facility, Nottingham Trent University, Nottingham, NG11 8NS, UK.
- Computing and Informatics Research Centre, Nottingham Trent University, Nottingham, NG11 8NS, UK.
| |
Collapse
|
2
|
Gupta S, Dubey AK, Singh R, Kalra MK, Abraham A, Kumari V, Laird JR, Al-Maini M, Gupta N, Singh I, Viskovic K, Saba L, Suri JS. Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans. Diagnostics (Basel) 2024; 14:1534. [PMID: 39061671 PMCID: PMC11275579 DOI: 10.3390/diagnostics14141534] [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: 05/04/2024] [Revised: 07/10/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.
Collapse
Affiliation(s)
- Siddharth Gupta
- Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India;
| | - Arun K. Dubey
- Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India; (A.K.D.); (N.G.)
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
| | - Ajith Abraham
- Department of Computer Science, Bennett University, Greater Noida 201310, India;
| | - Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON M5G 1N8, Canada;
| | - Neha Gupta
- Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India; (A.K.D.); (N.G.)
| | - Inder Singh
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Klaudija Viskovic
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Luca Saba
- Department of ECE, Idaho State University, Pocatello, ID 83209, USA;
| | - Jasjit S. Suri
- Department of ECE, Idaho State University, Pocatello, ID 83209, USA;
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India
- Department of Computer Science & Engineering, Symbiosis Institute of Technology, Nagpur Campus 440008, Symbiosis International (Deemed University), Pune 412115, India
| |
Collapse
|
3
|
Kolhar M, Al Rajeh AM, Kazi RNA. Augmenting Radiological Diagnostics with AI for Tuberculosis and COVID-19 Disease Detection: Deep Learning Detection of Chest Radiographs. Diagnostics (Basel) 2024; 14:1334. [PMID: 39001228 PMCID: PMC11240993 DOI: 10.3390/diagnostics14131334] [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: 05/11/2024] [Revised: 06/18/2024] [Accepted: 06/21/2024] [Indexed: 07/16/2024] Open
Abstract
In this research, we introduce a network that can identify pneumonia, COVID-19, and tuberculosis using X-ray images of patients' chests. The study emphasizes tuberculosis, COVID-19, and healthy lung conditions, discussing how advanced neural networks, like VGG16 and ResNet50, can improve the detection of lung issues from images. To prepare the images for the model's input requirements, we enhanced them through data augmentation techniques for training purposes. We evaluated the model's performance by analyzing the precision, recall, and F1 scores across training, validation, and testing datasets. The results show that the ResNet50 model outperformed VGG16 with accuracy and resilience. It displayed superior ROC AUC values in both validation and test scenarios. Particularly impressive were ResNet50's precision and recall rates, nearing 0.99 for all conditions in the test set. On the hand, VGG16 also performed well during testing-detecting tuberculosis with a precision of 0.99 and a recall of 0.93. Our study highlights the performance of our deep learning method by showcasing the effectiveness of ResNet50 over traditional approaches like VGG16. This progress utilizes methods to enhance classification accuracy by augmenting data and balancing them. This positions our approach as an advancement in using state-of-the-art deep learning applications in imaging. By enhancing the accuracy and reliability of diagnosing ailments such as COVID-19 and tuberculosis, our models have the potential to transform care and treatment strategies, highlighting their role in clinical diagnostics.
Collapse
Affiliation(s)
- Manjur Kolhar
- Department Health Informatics, College of Applied Medical Sciences, King Faisal University, Al-Hofuf 31982, Saudi Arabia
| | - Ahmed M Al Rajeh
- College of Applied Medical Sciences, King Faisal University, Al-Hofuf 31982, Saudi Arabia
| | - Raisa Nazir Ahmed Kazi
- College of Applied Medical Sciences, King Faisal University, Al-Hofuf 31982, Saudi Arabia
| |
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Singh J, Khanna NN, Rout RK, Singh N, Laird JR, Singh IM, Kalra MK, Mantella LE, Johri AM, Isenovic ER, Fouda MM, Saba L, Fatemi M, Suri JS. GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides. Sci Rep 2024; 14:7154. [PMID: 38531923 PMCID: PMC11344070 DOI: 10.1038/s41598-024-56786-9] [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: 07/11/2023] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.
Collapse
Affiliation(s)
- Jaskaran Singh
- Department of Computer Science, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Ranjeet K Rout
- Department of Computer Science and Engineering, NIT Srinagar, Hazratbal, Srinagar, India
| | - Narpinder Singh
- Department of Food Science, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Inder M Singh
- Advanced Cardiac and Vascular Institute, Sacramento, CA, USA
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA, 02115, USA
| | - Laura E Mantella
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Amer M Johri
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Esma R Isenovic
- Laboratory for Molecular Genetics and Radiobiology, University of Belgrade, Belgrade, Serbia
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Luca Saba
- Department of Neurology, University of Cagliari, Cagliari, Italy
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint LLC, Roseville, CA, 95661, USA.
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
Collapse
Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - 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, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
| |
Collapse
|
9
|
Albahlal BM. Emerging Technology-Driven Hybrid Models for Preventing and Monitoring Infectious Diseases: A Comprehensive Review and Conceptual Framework. Diagnostics (Basel) 2023; 13:3047. [PMID: 37835793 PMCID: PMC10572974 DOI: 10.3390/diagnostics13193047] [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: 06/30/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/15/2023] Open
Abstract
The emergence of the infectious diseases, such as the novel coronavirus, as a significant global health threat has emphasized the urgent need for effective treatments and vaccines. As infectious diseases become more common around the world, it is important to have strategies in place to prevent and monitor them. This study reviews hybrid models that incorporate emerging technologies for preventing and monitoring infectious diseases. It also presents a comprehensive review of the hybrid models employed for preventing and monitoring infectious diseases since the outbreak of COVID-19. The review encompasses models that integrate emerging and innovative technologies, such as blockchain, Internet of Things (IoT), big data, and artificial intelligence (AI). By harnessing these technologies, the hybrid system enables secure contact tracing and source isolation. Based on the review, a hybrid conceptual framework model proposes a hybrid model that incorporates emerging technologies. The proposed hybrid model enables effective contact tracing, secure source isolation using blockchain technology, IoT sensors, and big data collection. A hybrid model that incorporates emerging technologies is proposed as a comprehensive approach to preventing and monitoring infectious diseases. With continued research on and the development of the proposed model, the global efforts to effectively combat infectious diseases and safeguard public health will continue.
Collapse
Affiliation(s)
- Bader M Albahlal
- College of Computer and Information Sciences, Imam Muhammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia
| |
Collapse
|