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Islam MS, Kalmady SV, Hindle A, Sandhu R, Sun W, Sepehrvand N, Greiner R, Kaul P. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications. Can J Cardiol 2024:S0828-282X(24)00523-3. [PMID: 38992812 DOI: 10.1016/j.cjca.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.
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Affiliation(s)
- Md Saiful Islam
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sunil Vasu Kalmady
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Russel Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Padma Kaul
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
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Nolin-Lapalme A, Corbin D, Tastet O, Avram R, Hussin JG. Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology. Can J Cardiol 2024:S0828-282X(24)00357-X. [PMID: 38735528 DOI: 10.1016/j.cjca.2024.04.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2024] Open
Abstract
In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area for its technological advancements and clinical application. In this review we explore the complex issue of data bias, specifically addressing those encountered during the development and implementation of AI tools in cardiology. We dissect the origins and effects of these biases, which challenge their reliability and widespread applicability in health care. Using a case study, we highlight the complexities involved in addressing these biases from a clinical viewpoint. The goal of this review is to equip researchers and clinicians with the practical knowledge needed to identify, understand, and mitigate these biases, advocating for the creation of AI solutions that are not just technologically sound, but also fair and effective for all patients.
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Affiliation(s)
- Alexis Nolin-Lapalme
- Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada; Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada; Mila - Québec AI Institute, Montreal, Quebec, Canada; Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, Quebec, Canada.
| | - Denis Corbin
- Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Olivier Tastet
- Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Robert Avram
- Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada; Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada; Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, Quebec, Canada
| | - Julie G Hussin
- Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada; Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada; Mila - Québec AI Institute, Montreal, Quebec, Canada
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Lin CH, Liu ZY, Chen JS, Fann YC, Wen MS, Kuo CF. ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram. Biomed J 2024:100732. [PMID: 38697480 DOI: 10.1016/j.bj.2024.100732] [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: 08/25/2023] [Revised: 03/12/2024] [Accepted: 04/18/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling. METHODS This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices. RESULTS The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]). CONCLUSION ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.
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Affiliation(s)
- Ching-Heng Lin
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan
| | - Zhi-Yong Liu
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Jung-Sheng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Yang C Fann
- Division of Intramural Research, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, United States
| | - Ming-Shien Wen
- Division of Cardiology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chang-Fu Kuo
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
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4
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Moreno-Sánchez PA, García-Isla G, Corino VDA, Vehkaoja A, Brukamp K, van Gils M, Mainardi L. ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review. Comput Biol Med 2024; 172:108235. [PMID: 38460311 DOI: 10.1016/j.compbiomed.2024.108235] [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: 08/11/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/11/2024]
Abstract
Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.
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Affiliation(s)
| | - Guadalupe García-Isla
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Valentina D A Corino
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Mark van Gils
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Luca Mainardi
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
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Boribalburephan A, Treewaree S, Tantisiriwat N, Yindeengam A, Achakulvisut T, Krittayaphong R. Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning. Sci Rep 2024; 14:7523. [PMID: 38553581 PMCID: PMC10980683 DOI: 10.1038/s41598-024-58131-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular parameters, conventionally determined using cardiac magnetic resonance (CMR). However, given the high cost and limited availability of CMR in resource-constrained settings, electrocardiograms (ECGs) are a cost-effective alternative. We developed computer vision-based multi-task deep learning models to analyze 12-lead ECG 2D images, predicting MS and LVEF < 50%. Our dataset comprises 14,052 ECGs with clinical features, utilizing ground truth labels from CMR. Our top-performing model achieved AUC values of 0.838 (95% CI 0.812-0.862) for MS and 0.939 (95% CI 0.921-0.954) for LVEF < 50% classification, outperforming cardiologists. Moreover, MS predictions in a prevalence-specific test dataset recorded an AUC of 0.812 (95% CI 0.810-0.814). Extracted 1D signals from ECG images yielded inferior performance, compared to the 2D approach. In conclusion, our results demonstrate the potential of computer-based MS and LVEF < 50% classification from ECG scan images in clinical screening offering a cost-effective alternative to CMR.
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Affiliation(s)
- Atirut Boribalburephan
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
- Looloo Technology, Bangkok, Thailand
| | - Sukrit Treewaree
- Division of Cardiology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand
| | - Noppawat Tantisiriwat
- Division of Cardiology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand
| | - Ahthit Yindeengam
- Her Majesty Cardiac Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Titipat Achakulvisut
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
| | - Rungroj Krittayaphong
- Division of Cardiology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand.
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Ghabri H, Alqahtani MS, Ben Othman S, Al-Rasheed A, Abbas M, Almubarak HA, Sakli H, Abdelkarim MN. Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers. Sci Rep 2023; 13:17904. [PMID: 37863944 PMCID: PMC10589237 DOI: 10.1038/s41598-023-44689-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 10/11/2023] [Indexed: 10/22/2023] Open
Abstract
Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide.
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Affiliation(s)
- Haifa Ghabri
- MACS Laboratory, National Engineering School of Gabes, University of Gabes, 6029, Gabès, Tunisia
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia
- BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE17RH, UK
| | - Soufiene Ben Othman
- PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia.
| | - Amal Al-Rasheed
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia
| | - Hassan Ali Almubarak
- Division of Radiology, Department of Medicine, College of Medicine and Surgery, King Khalid University (KKU), Abha, Aseer, Saudi Arabia
| | - Hedi Sakli
- EITA Consulting, 5 Rue Du Chant des Oiseaux, 78360, Montesson, Montesson, France
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7
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Souid A, Alsubaie N, Soufiene BO, Alqahtani MS, Abbas M, Jambi LK, Sakli H. Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach. Sci Rep 2023; 13:16619. [PMID: 37789095 PMCID: PMC10547797 DOI: 10.1038/s41598-023-42366-w] [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: 04/13/2023] [Accepted: 09/09/2023] [Indexed: 10/05/2023] Open
Abstract
Detecting lung pathologies is critical for precise medical diagnosis. In the realm of diagnostic methods, various approaches, including imaging tests, physical examinations, and laboratory tests, contribute to this process. Of particular note, imaging techniques like X-rays, CT scans, and MRI scans play a pivotal role in identifying lung pathologies with their non-invasive insights. Deep learning, a subset of artificial intelligence, holds significant promise in revolutionizing the detection and diagnosis of lung pathologies. By leveraging expansive datasets, deep learning algorithms autonomously discern intricate patterns and features within medical images, such as chest X-rays and CT scans. These algorithms exhibit an exceptional capacity to recognize subtle markers indicative of lung diseases. Yet, while their potential is evident, inherent limitations persist. The demand for abundant labeled data during training and the susceptibility to data biases challenge their accuracy. To address these formidable challenges, this research introduces a tailored computer-assisted system designed for the automatic retrieval of annotated medical images that share similar content. At its core lies an intelligent deep learning-based features extractor, adept at simplifying the retrieval of analogous images from an extensive chest radiograph database. The crux of our innovation rests upon the fusion of YOLOv5 and EfficientNet within the features extractor module. This strategic fusion synergizes YOLOv5's rapid and efficient object detection capabilities with EfficientNet's proficiency in combating noisy predictions. The result is a distinctive amalgamation that redefines the efficiency and accuracy of features extraction. Through rigorous experimentation conducted on an extensive and diverse dataset, our proposed solution decisively surpasses conventional methodologies. The model's achievement of a mean average precision of 0.488 with a threshold of 0.9 stands as a testament to its effectiveness, overshadowing the results of YOLOv5 + ResNet and EfficientDet, which achieved 0.234 and 0.257 respectively. Furthermore, our model demonstrates a marked precision improvement, attaining a value of 0.864 across all pathologies-a noteworthy leap of approximately 0.352 compared to YOLOv5 + ResNet and EfficientDet. This research presents a significant stride toward enhancing radiologists' workflow efficiency, offering a refined and proficient tool for retrieving analogous annotated medical images.
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Affiliation(s)
- Abdelbaki Souid
- MACS Research Laboratory RL16ES22, National Engineering School of Gabes, Gabes, Tunisia
| | - Najah Alsubaie
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Ben Othman Soufiene
- PRINCE Laboratory Research, ISITcom, University of Sousse, Hammam Sousse, Tunisia.
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia
- BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE17RH, UK
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia
| | - Layal K Jambi
- Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, 11433, Riyadh, Saudi Arabia
| | - Hedi Sakli
- MACS Research Laboratory RL16ES22, National Engineering School of Gabes, Gabes, Tunisia
- EITA Consulting, 5 Rue Du Chant Des Oiseaux, 78360, Montesson, France
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8
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Li X, Xu Z, Wang Y. PSO-MCKD-MFFResnet based fault diagnosis algorithm for hydropower units. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:14117-14135. [PMID: 37679128 DOI: 10.3934/mbe.2023631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Due to the coupling effect of external environmental noise and vibration noise, the feature rate of the original hydroelectric unit fault signal is not prominent, which will affect the performance of fault diagnosis algorithms. To solve the above problems, this paper proposes a PSO-MCKD-MFFResnet algorithm for fault diagnosis of hydropower units (Particle swarm optimization, PSO; Maximum correlation kurtosis deconvolution, MCKD; Multi-scale feature fusion residual network, MFFResnet). In practical applications, the selection of key parameters in the traditional MCKD method is heavily dependent on prior knowledge. First, this paper proposes a PSO-MCKD enhancement algorithm for fault features, which uses the PSO algorithm to search for the influencing parameters of MCKD to enhance the features from the original fault signal. Second, a fault feature diagnosis algorithm based on MFFResnet is proposed to improve the utilization of local features. The multi-scale residual module is used to extract features at different scales and then put the enhanced signal into MFFResnet for training and classification. The experimental results show that our approach can accurately and effectively classify the fault types of hydropower units, with an accuracy rate of 98.85. It is superior to other representative algorithms in different indicators and has a good stability.
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Affiliation(s)
- Xu Li
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, Shaanxi, China
| | - Zhuofei Xu
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, Shaanxi, China
| | - Yimin Wang
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, Shaanxi, China
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Serhal H, Abdallah N, Marion JM, Chauvet P, Oueidat M, Humeau-Heurtier A. An EMD-based approach for atrial fibrillation classification using wavelets and convolutional neural network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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10
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Automatic Detection of Cracks in Cracked Tooth Based on Binary Classification Convolutional Neural Networks. Appl Bionics Biomech 2022; 2022:9333406. [PMID: 36245930 PMCID: PMC9553657 DOI: 10.1155/2022/9333406] [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: 08/10/2022] [Accepted: 08/23/2022] [Indexed: 11/18/2022] Open
Abstract
Cracked tooth syndrome is a commonly encountered disease in dentistry, which is often accompanied by dramatic painful responses from occlusion and temperature stimulation. Current clinical diagnostic trials include traditional methods (such as occlusion test, probing, cold stimulation, etc.) and X-rays based medical imaging (periapical radiography (PR), cone-beam computed tomography (CBCT), etc.). However, these methods are strongly dependent on the experience of the clinicians, and some inconspicuous cracks are also extremely easy to be overlooked by visual observation, which will definitely affect the subsequent treatments. Inspired by the achievements of applying deep convolutional neural networks (CNNs) in crack detection in engineering, this article proposes an image-based crack detection method using a deep CNN classifier in combination with a sliding window algorithm. A CNN model is designed by modifying the size of the input layer and adding a fully connected layer with 2 units based on the ResNet50, and then, the proposed CNN is trained and validated with a self-prepared cracked tooth dataset including 20,000 images. By comparing validation accuracy under seven different learning rates, 10−5 is chosen as the best learning rate for the following testing process. The trained CNN is tested on 100 images with 1920 × 1080-pixel resolutions, which achieves an average accuracy of 90.39%. The results show that the proposed method can effectively detect cracks in images under various conditions (stained, overexplosion, images affected by other diseases). The proposed method in this article provides doctors with a more intelligent diagnostic solution, and it is not only suitable for optical photographs but also for automated diagnosis of other medical imaging images.
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11
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Trust-Based Smart Contract for Automated Agent to Agent Communication. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5136865. [PMID: 36164421 PMCID: PMC9509257 DOI: 10.1155/2022/5136865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 08/07/2022] [Accepted: 08/25/2022] [Indexed: 11/17/2022]
Abstract
Blockchain technology is now regarded as one of the most interesting and possibly innovative technologies. It enables information to be stored and exchanged securely and transparently without the need for a centralized authority to regulate it. Some of the primary benefits of this technology are the atomicity of the stored data. Given its features, this technology has the potential to provide answers to challenges encountered in a very sensitive sector, namely, Internet of Vehicles (IoV). In IoV, vehicles and service providers autonomously capture and produce data without human intervention. This exchanged data must meet certain criteria such as decentralization, automation, security, and stakeholder trust management. To overcome these challenges, the integration of blockchain technology and multi-agent systems is a key solution. Based on smart contracts, the proposed solution consists of exploiting role-based access control (RBAC) and attribute-based access control (ABAC) techniques. This solution removes the central authority (CA) to reduce maintenance costs and eliminate legacy threats from centralized systems. The results, obtained from consumption costs, show that the developed platform is characterized by security, availability, and privacy.
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