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Chen L, Huang SH, Wang TH, Lan TY, Tseng VS, Tsao HM, Wang HH, Tang GJ. Deep learning-based automatic left atrial appendage filling defects assessment on cardiac computed tomography for clinical and subclinical atrial fibrillation patients. Heliyon 2023; 9:e12945. [PMID: 36699283 PMCID: PMC9868534 DOI: 10.1016/j.heliyon.2023.e12945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/04/2023] [Accepted: 01/10/2023] [Indexed: 01/15/2023] Open
Abstract
Rationale and objectives Selecting region of interest (ROI) for left atrial appendage (LAA) filling defects assessment can be time consuming and prone to subjectivity. This study aimed to develop and validate a novel artificial intelligence (AI), deep learning (DL) based framework for automatic filling defects assessment on CT images for clinical and subclinical atrial fibrillation (AF) patients. Materials and methods A total of 443,053 CT images were used for DL model development and testing. Images were analyzed by the AI framework and expert cardiologists/radiologists. The LAA segmentation performance was evaluated using Dice coefficient. The agreement between manual and automatic LAA ROI selections was evaluated using intraclass correlation coefficient (ICC) analysis. Receiver operating characteristic (ROC) curve analysis was used to assess filling defects based on the computed LAA to ascending aorta Hounsfield unit (HU) ratios. Results A total of 210 patients (Group 1: subclinical AF, n = 105; Group 2: clinical AF with stroke, n = 35; Group 3: AF for catheter ablation, n = 70) were enrolled. The LAA volume segmentation achieved 0.931-0.945 Dice scores. The LAA ROI selection demonstrated excellent agreement (ICC ≥0.895, p < 0.001) with manual selection on the test sets. The automatic framework achieved an excellent AUC score of 0.979 in filling defects assessment. The ROC-derived optimal HU ratio threshold for filling defects detection was 0.561. Conclusion The novel AI-based framework could accurately segment the LAA region and select ROIs while effectively avoiding trabeculae for filling defects assessment, achieving close-to-expert performance. This technique may help preemptively detect the potential thromboembolic risk for AF patients.
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Key Words
- AA, Ascending aorta
- AF, Atrial fibrillation
- AI, Artificial intelligence
- AUC, Area under the ROC curve
- Artificial intelligence
- Atrial fibrillation
- CI, Confidence interval
- Computed tomography
- DL, Deep learning
- Deep learning
- ECG, Electrocardiogram
- HU, Hounsfield unit
- ICC, Intraclass correlation coefficient
- LAA, Left atrial appendage
- Left atrial appendage
- ROC, Receiver operating characteristics
- ROI, Region of interest
- SD, Standard deviation
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Affiliation(s)
- Ling Chen
- Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sung-Hao Huang
- Division of Cardiology, Department of Internal Medicine, National Yang Ming Chiao Tung University Hospital, Yi-Lan, Taiwan,Corresponding author.
| | - Tzu-Hsiang Wang
- Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tzuo-Yun Lan
- Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Vincent S. Tseng
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Hsuan-Ming Tsao
- Division of Cardiology, Department of Internal Medicine, National Yang Ming Chiao Tung University Hospital, Yi-Lan, Taiwan,School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hsueh-Han Wang
- Department of Radiology, National Yang Ming Chiao Tung University Hospital, Yi-Lan, Taiwan
| | - Gau-Jun Tang
- Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan
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Ferruz N, Heinzinger M, Akdel M, Goncearenco A, Naef L, Dallago C. From sequence to function through structure: Deep learning for protein design. Comput Struct Biotechnol J 2022; 21:238-250. [PMID: 36544476 PMCID: PMC9755234 DOI: 10.1016/j.csbj.2022.11.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/05/2022] [Accepted: 11/05/2022] [Indexed: 11/20/2022] Open
Abstract
The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be used to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field.
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Key Words
- ADMM, Alternating Direction Method of Multipliers
- CNN, Convolutional Neural Network
- DL, Deep learning
- Deep learning
- Drug discovery
- FNN, fully-connected neural network
- GAN, Generative Adversarial Network
- GCN, Graph Convolutional Network
- GNN, Graph Neural Network
- GO, Gene Ontology
- GVP, Geometric Vector Perceptron
- LSTM, Long-Short Term Memory
- MLP, Multilayer Perceptron
- MSA, Multiple Sequence Alignment
- NLP, Natural Language Processing
- NSR, Natural Sequence Recovery
- Protein design
- Protein language models
- Protein prediction
- VAE, Variational Autoencoder
- pLM, protein Language Model
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Affiliation(s)
- Noelia Ferruz
- Institute of Informatics and Applications, University of Girona, Girona, Spain
- Department of Biochemistry, University of Bayreuth, Bayreuth, Germany
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics & Computational Biology, Technische Universität München, 85748 Garching, Germany
| | - Mehmet Akdel
- VantAI, 151 W 42nd Street, New York, NY 10036, United States
| | | | - Luca Naef
- VantAI, 151 W 42nd Street, New York, NY 10036, United States
| | - Christian Dallago
- Department of Informatics, Bioinformatics & Computational Biology, Technische Universität München, 85748 Garching, Germany
- VantAI, 151 W 42nd Street, New York, NY 10036, United States
- NVIDIA DE GmbH, Einsteinstraße 172, 81677 München, Germany
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Xu HL, Gong TT, Liu FH, Chen HY, Xiao Q, Hou Y, Huang Y, Sun HZ, Shi Y, Gao S, Lou Y, Chang Q, Zhao YH, Gao QL, Wu QJ. Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis. EClinicalMedicine 2022; 53:101662. [PMID: 36147628 PMCID: PMC9486055 DOI: 10.1016/j.eclinm.2022.101662] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/25/2022] [Accepted: 08/30/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time. METHODS The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611. FINDINGS Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95). Analysis for different algorithms revealed a pooled SE of 89% (85-92%) and SP of 88% (82-92%) for machine learning; and a pooled SE of 88% (84-91%) and SP of 84% (80-87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (≤300 or >300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (≥3 domain low risk or < 3 domain low risk). INTERPRETATION AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies. FUNDING This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).
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Key Words
- AI, Artificial intelligence
- AUC, Area Under the Curve
- Artificial intelligence
- CT, Computed Tomography
- DL, Deep learning
- ML, Machine learning
- MRI, Magnetic Resonance Imaging
- Medical imaging
- Meta-analysis
- OC, Ovarian cancer
- Ovarian cancer
- SE, Sensitivity
- SP, Specificity
- US, Ultrasound
- XAI, Explainable artificial intelligence
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Affiliation(s)
- He-Li Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hong-Yu Chen
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ying Huang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hong-Zan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu Shi
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yan Lou
- Department of Intelligent Medicine, China Medical University, China
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu-Hong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qing-Lei Gao
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynecology and Obstetrics, Tongji Hospital, Wuhan, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Corresponding author at: Department of Clinical Epidemiology, Department of Obstetrics and Gynecology, Clinical Research Center, Shengjing Hospital of China Medical University, Address: No. 36, San Hao Street, Shenyang, Liaoning 110004, PR China.
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Meng L, Chan WS, Huang L, Liu L, Chen X, Zhang W, Wang F, Cheng K, Sun H, Wong KC. Mini-review: Recent advances in post-translational modification site prediction based on deep learning. Comput Struct Biotechnol J 2022; 20:3522-32. [PMID: 35860402 DOI: 10.1016/j.csbj.2022.06.045] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/23/2022] Open
Abstract
Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.
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Key Words
- AAindex, Amino acid index
- ATP, Adenosine triphosphate
- AUC, Area under curve
- Ac, Acetylation
- BE, Binary encoding
- BLOSUM, Blocks substitution matrix
- Bi-LSTM, Bidirectional LSTM
- CKSAAP, Composition of k-spaced amino acid Pairs
- CNN, Convolutional neural network
- CNNOH, CNN with the one-hot encoding
- CNNWE, CNN with the word-embedding encoding
- CNNrgb, CNN red green blue
- CV, Cross-validation
- DC-CNN, Densely connected convolutional neural network
- DL, Deep learning
- DNNs, Deep neural networks
- Deep learning
- E. coli, Escherichia coli
- EBGW, Encoding based on grouped weight
- EGAAC, Enhanced grouped amino acids content
- IG, Information gain
- K, Lysine
- KNN, k nearest neighbor
- LASSO, Least absolute shrinkage and selection operator
- LSTM, Long short-term memory
- LSTMWE, LSTM with the word-embedding encoding
- M.musculus, Mus musculus
- MDC, Modular densely connected convolutional networks
- MDCAN, Multilane dense convolutional attention network
- ML, Machine learning
- MLP, Multilayer perceptron
- MMI, Multivariate mutual information
- Machine learning
- Mass spectrometry
- NMBroto, Normalized Moreau-Broto autocorrelation
- P, Proline
- PSP, PhosphoSitePlus
- PSSM, Position-specific scoring matrix
- PTM, Post-translational modifications
- Ph, Phosphorylation
- Post-translational modification
- Prediction
- PseAAC, Pseudo-amino acid composition
- R, Arginine
- RF, Random forest
- RNN, Recurrent neural network
- ROC, Receiver operating characteristic
- S, Serine
- S. typhimurium, Salmonella typhimurium
- S.cerevisiae, Saccharomyces cerevisiae
- SE, Squeeze and excitation
- SEV, Split to Equal Validation
- ST, Source and target
- SUMO, Small ubiquitin-like modifier
- SVM, Support vector machines
- T, Threonine
- Ub, Ubiquitination
- Y, Tyrosine
- ZSL, Zero-shot learning
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Hoffmann B, Gerst R, Cseresnyés Z, Foo W, Sommerfeld O, Press AT, Bauer M, Figge MT. Spatial quantification of clinical biomarker pharmacokinetics through deep learning-based segmentation and signal-oriented analysis of MSOT data. Photoacoustics 2022; 26:100361. [PMID: 35541023 PMCID: PMC9079355 DOI: 10.1016/j.pacs.2022.100361] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 04/07/2022] [Accepted: 04/22/2022] [Indexed: 06/14/2023]
Abstract
Although multispectral optoacoustic tomography (MSOT) significantly evolved over the last several years, there is a lack of quantitative methods for analysing this type of image data. Current analytical methods characterise the MSOT signal in manually defined regions of interest outlining selected tissue areas. These methods demand expert knowledge of the sample anatomy, are time consuming, highly subjective and prone to user bias. Here we present our fully automated open-source MSOT cluster analysis toolkit Mcat that was designed to overcome these shortcomings. It employs a deep learning-based approach for initial image segmentation followed by unsupervised machine learning to identify regions of similar signal kinetics. It provides an objective and automated approach to quantify the pharmacokinetics and extract the biodistribution of biomarkers from MSOT data. We exemplify our generally applicable analysis method by quantifying liver function in a preclinical sepsis model whilst highlighting the advantages of our new approach compared to the severe limitations of existing analysis procedures.
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Key Words
- AUC, Area under the curve
- Biomarkers
- DAG, Directed acyclic graph
- DL, Deep learning
- Deep learning
- GUI, Graphical user interface
- ICG, Indocyanine green
- ImageJ plugin
- MSE, Mean squared error
- MSOT, Multispectral optoacoustic tomography
- Mcat, MSOT cluster analysis toolkit
- Multispectral optoacoustic tomography
- PCI, Peritoneal contamination and infection
- Pharmacokinetics
- Quantitative image analysis
- ROI, Region of interest
- Sepsis
- WAC, Weighted-average curve
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Affiliation(s)
- Bianca Hoffmann
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute (HKI), Beutenbergstr. 11a, 07745 Jena, Germany
| | - Ruman Gerst
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute (HKI), Beutenbergstr. 11a, 07745 Jena, Germany
- Faculty of Biological Sciences, Friedrich Schiller University Jena, Bachstr. 18k, 07743 Jena, Germany
| | - Zoltán Cseresnyés
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute (HKI), Beutenbergstr. 11a, 07745 Jena, Germany
| | - WanLing Foo
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Oliver Sommerfeld
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Adrian T. Press
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Medical Faculty, Friedrich Schiller University Jena, Kastanienstr. 1, 07747 Jena, Germany
| | - Michael Bauer
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Marc Thilo Figge
- Research Group Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute (HKI), Beutenbergstr. 11a, 07745 Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Institute of Microbiology, Faculty of Biological Sciences, Friedrich Schiller University Jena, Neugasse 25, 07743 Jena, Germany
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Mohammedqasem R, Mohammedqasim H, Ata O. Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network. Comput Electr Eng 2022; 100:107971. [PMID: 35399912 PMCID: PMC8985446 DOI: 10.1016/j.compeleceng.2022.107971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 05/03/2023]
Abstract
The coronavirus pandemic has affected people all over the world and posed a great challenge to international health systems. To aid early detection of coronavirus disease-2019 (COVID-19), this study proposes a real-time detection system based on the Internet of Things framework. The system collects real-time data from users to determine potential coronavirus cases, analyses treatment responses for people who have been treated, and accurately collects and analyses the datasets. Artificial intelligence-based algorithms are an alternative decision-making solution to extract valuable information from clinical data. This study develops a deep learning optimisation system that can work with imbalanced datasets to improve the classification of patients. A synthetic minority oversampling technique is applied to solve the problem of imbalance, and a recursive feature elimination algorithm is used to determine the most effective features. After data balance and extraction of features, the data are split into training and testing sets for validating all models. The experimental predictive results indicate good stability and compatibility of the models with the data, providing maximum accuracy of 98% and precision of 97%. Finally, the developed models are demonstrated to handle data bias and achieve high classification accuracy for patients with COVID-19. The findings of this study may be useful for healthcare organisations to properly prioritise assets.
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Key Words
- ANN, Artificial Neural Network
- AUC, Area Under Curve
- CNN, Convolutional Neural Network
- COVID-19
- COVID-19, Coronavirus disease
- DL, Deep learning
- Imbalanced Dataset
- Internet of Things
- IoT, Internet of Things
- ML, Machine learning
- RFE, Recursive Feature Elimination
- RNN, Recurrent Neural Network
- Recursive feature elimination
- SMOTE, Synthetic Minority Oversampling Technique
- Synthetic minority oversampling technique
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Affiliation(s)
- Roa'a Mohammedqasem
- Department Of ECE, Institute Of Science, Altinbas University, Istanbul, Turkey
| | | | - Oguz Ata
- Department Of ECE, Institute Of Science, Altinbas University, Istanbul, Turkey
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Kim C, Cho HH, Choi JY, Franks TJ, Han J, Choi Y, Lee SH, Park H, Lee KS. Pleomorphic carcinoma of the lung: Prognostic models of semantic, radiomics and combined features from CT and PET/CT in 85 patients. Eur J Radiol Open 2021; 8:100351. [PMID: 34041307 PMCID: PMC8141891 DOI: 10.1016/j.ejro.2021.100351] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 05/03/2021] [Accepted: 05/08/2021] [Indexed: 02/06/2023] Open
Abstract
Introduction To demonstrate semantic, radiomics, and the combined risk models related to the prognoses of pulmonary pleomorphic carcinomas (PCs). Methods We included 85 patients (M:F = 71:14; age, 35–88 [mean, 63 years]) whose imaging features were divided into training (n = 60) and test (n = 25) sets. Nineteen semantic and 142 radiomics features related to tumors were computed. Semantic risk score (SRS) model was built using the Cox-least absolute shrinkage and selection operator (LASSO) approach. Radiomics risk score (RRS) from CT and PET features and combined risk score (CRS) adopting both semantic and radiomics features were also constructed. Risk groups were stratified by the median of the risk scores of the training set. Survival analysis was conducted with the Kaplan-Meier plots. Results Of 85 PCs, adenocarcinoma was the most common epithelial component found in 63 (73 %) tumors. In SRS model, four features were stratified into high- and low-risk groups (HR, 4.119; concordance index ([C-index], 0.664) in the test set. In RRS model, five features helped improve the stratification (HR, 3.716; C-index, 0.591) and in CRS model, three features helped perform the best stratification (HR, 4.795; C-index, 0.617). The two significant features of CRS models were the SUVmax and the histogram feature of energy ([CT Firstorder Energy]). Conclusion In PCs of the lungs, the combined model leveraging semantic and radiomics features provides a better prognosis compared to using semantic and radiomics features separately. The high SUVmax of solid portion (CT Firstorder Energy) of tumors is associated with poor prognosis in lung PCs.
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Key Words
- C-index, Concordance index
- CRS, Combined risk score
- DL, Deep learning
- GCLM, Gray-level co-occurrence matrix
- HR, Hazard ration
- ICC, Intra-class correlation
- ISZM, Intensity size zone matrix
- KRAS, Kirsten rat sarcoma viral oncogene homolog
- LASSO, Least absolute shrinkage and selection operator
- LDA, Low density area
- Lung
- MRI, Magnetic resonance imaging
- MTV, Metabolic tumor volume
- Non-small cell carcinoma
- PC, Pleomorphic carcinoma
- PET/CT, Positron emission tomography/Computed tomography
- Pleomorphic carcinoma
- Prognosis
- ROI, Region of interest
- RRS, Radiomics risk score
- Radiomics
- SRS, Semantic risk score
- SUVavg, Average standardized uptake value
- SUVmax, Maximum standardized uptake value
- TLG, Total lesion glycolysis
- VOI, Volume of interest
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Affiliation(s)
- Chohee Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Hwan-Ho Cho
- Department of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Teri J Franks
- Department of Pulmonary and Mediastinal Pathology, Department of Defense, The Joint Pathology Center, Silver Spring, MD, USA
| | - Joungho Han
- Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Yeonu Choi
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | - Kyung Soo Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
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Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos Solitons Fractals 2020; 140:110212. [PMID: 32839642 PMCID: PMC7437542 DOI: 10.1016/j.chaos.2020.110212] [Citation(s) in RCA: 153] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 08/16/2020] [Indexed: 05/07/2023]
Abstract
COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.
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Key Words
- AI, Artificial intelligence
- AR, Autoregressive
- ARIMA, Autoregressive integrated moving average
- Bi-LSTM
- Bi-LSTM, Bidirectional long short term memory
- COVID-19
- Corona virus
- DL, Deep learning
- Deep learning models
- GRU
- GRU, Gated recurrent network
- LSTM, Long short term memory
- MERS, Middle East respiratory syndrome
- NN, Neural network
- RF, Random forest
- RNN, Recurrent neural network
- SARIMA, Seasonal autoregressive integrated moving average
- SARS, Severe acute respiratory syndrome
- SIR, Susceptible-infective-removed
- SVR, Support vector machine
- WHO, World health organization
- epidemic prediction
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Affiliation(s)
- Farah Shahid
- Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan
| | - Aneela Zameer
- Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan
| | - Muhammad Muneeb
- Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan
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