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Mehri M, Calmon G, Odille F, Oster J. A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision. SENSORS (BASEL, SWITZERLAND) 2023; 23:2288. [PMID: 36850889 PMCID: PMC9963088 DOI: 10.3390/s23042288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.
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Affiliation(s)
- Maroua Mehri
- Epsidy, 54000 Nancy, France
- Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Université de Sousse, Sousse 4023, Tunisia
| | | | - Freddy Odille
- Epsidy, 54000 Nancy, France
- IADI-Imagerie Adaptative Diagnostique et Interventionnelle, Inserm U1254, Université de Lorraine, 54000 Nancy, France
- CIC-IT 1433, Inserm, CHRU de Nancy, Université de Lorraine, 54000 Nancy, France
| | - Julien Oster
- IADI-Imagerie Adaptative Diagnostique et Interventionnelle, Inserm U1254, Université de Lorraine, 54000 Nancy, France
- CIC-IT 1433, Inserm, CHRU de Nancy, Université de Lorraine, 54000 Nancy, France
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Bilbao A, Ross DH, Lee JY, Donor MT, Williams SM, Zhu Y, Ibrahim YM, Smith RD, Zheng X. MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry. J Proteome Res 2023; 22:508-513. [PMID: 36414245 PMCID: PMC9898216 DOI: 10.1021/acs.jproteome.2c00313] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.
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Affiliation(s)
- Aivett Bilbao
- Pacific Northwest National Laboratory, Richland, WA, 99352, USA,Corresponding authors Aivett Bilbao – Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, United States; .; Xueyun Zheng – Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, United States;
| | - Dylan H. Ross
- Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Joon-Yong Lee
- Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Micah T. Donor
- Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | | | - Ying Zhu
- Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | | | | | - Xueyun Zheng
- Pacific Northwest National Laboratory, Richland, WA, 99352, USA,Corresponding authors Aivett Bilbao – Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, United States; .; Xueyun Zheng – Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, United States;
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Kedar S, Khazanchi D. Neurology education in the era of artificial intelligence. Curr Opin Neurol 2023; 36:51-58. [PMID: 36367213 DOI: 10.1097/wco.0000000000001130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
PURPOSE OF REVIEW The practice of neurology is undergoing a paradigm shift because of advances in the field of data science, artificial intelligence, and machine learning. To ensure a smooth transition, physicians must have the knowledge and competence to apply these technologies in clinical practice. In this review, we describe physician perception and preparedness, as well as current state for clinical applications of artificial intelligence and machine learning in neurology. RECENT FINDINGS Digital health including artificial intelligence-based/machine learning-based technology has made significant inroads into various aspects of healthcare including neurological care. Surveys of physicians and healthcare stakeholders suggests an overall positive perception about the benefits of artificial intelligence/machine learning in clinical practice. This positive perception is tempered by concerns for lack of knowledge and limited opportunities to build competence in artificial intelligence/machine learning technology. Literature about neurologist's perception and preparedness towards artificial intelligence/machine learning-based technology is scant. There are very few opportunities for physicians particularly neurologists to learn about artificial intelligence/machine learning-based technology. SUMMARY Neurologists have not been surveyed about their perception and preparedness to adopt artificial intelligence/machine learning-based technology in clinical practice. We propose development of a practical artificial intelligence/machine learning curriculum to enhance neurologists' competence in these newer technologies.
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Affiliation(s)
- Sachin Kedar
- Department of Ophthalmology
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia
| | - Deepak Khazanchi
- Department of Information Systems & Quantitative Analysis, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, Nebraska, USA
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Chou WC, Lin Z. Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling. Toxicol Sci 2023; 191:1-14. [PMID: 36156156 PMCID: PMC9887681 DOI: 10.1093/toxsci/kfac101] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.
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Affiliation(s)
- Wei-Chun Chou
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32608, USA
| | - Zhoumeng Lin
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32608, USA
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A novel deep learning simulation to predict radon activity concentration in soil layers. J Radioanal Nucl Chem 2023. [DOI: 10.1007/s10967-022-08735-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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KAiPP: An interaction recommendation approach for knowledge aided intelligent process planning with reinforcement learning. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Engel E, Engel N. A Review on Machine Learning Applications for Solar Plants. SENSORS (BASEL, SWITZERLAND) 2022; 22:9060. [PMID: 36501762 PMCID: PMC9738664 DOI: 10.3390/s22239060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/20/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant's system; proposes a simple but effective integration scheme of an ML sensor solar plant system's implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.
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Doya K, Friston K, Sugiyama M, Tenenbaum J. Neural Networks special issue on Artificial Intelligence and Brain Science. Neural Netw 2022; 155:328-329. [PMID: 36099665 DOI: 10.1016/j.neunet.2022.08.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Kenji Doya
- Okinawa Institute of Science and Technology Graduate University, Japan.
| | | | | | - Josh Tenenbaum
- Massachusetts Institute of Technology, United States of America
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