14 research outputs found
Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation
No full textExploring the missing values is an essential but challenging issue due to the complex latent spatio-temporal correlation and dynamic nature of time series. Owing to the outstanding performance in dealing with structure learning potentials, Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) are often used to capture such complex spatio-temporal features in multivariate time series. However, these data-driven models often fail to capture the essential spatio-temporal relationships when significant signal corruption occurs. Additionally, calculating the high-order neighbor nodes in these models is of high computational complexity. To address these problems, we propose a novel higher-order spatio-temporal physics-incorporated GNN (HSPGNN). Firstly, the dynamic Laplacian matrix can be obtained by the spatial attention mechanism. Then, the generic inhomogeneous partial differential equation (PDE) of physical dynamic systems is used to construct the dynamic higher-order spatio-temporal GNN adaptively to obtain the missing time series values. Moreover, we estimate the missing impact by Normalizing Flows (NF) to evaluate the importance of each node in the graph for better explainability. Experimental results on four benchmark datasets demonstrate the effectiveness of HSPGNN and the superior performance when combining various order neighbor nodes. Also, graph-like optical flow, dynamic graphs, and missing impact can be obtained naturally by HSPGNN, which provides better dynamic analysis and explanation than traditional data-driven models. © 2024 Owner/Author.</p
A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer’s disease, and mild cognitive impairment using brain 18F-FDG PET
No full textPurpose: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers.Materials and methods: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention.Results: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders.Conclusion: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus. © 2021. The Author(s).</p
A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimers disease, and mild cognitive impairment using brain 18F-FDG PET
No full textPurpose The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimers disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimers disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare models performance to that of multiple expert nuclear medicine physicians readers. Materials and methods Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimers disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The models performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.Funding Agencies|Halmstad University; Analytic Imaging Diagnostics Arena (AIDA) initiative - VINNOVA [2017-02447]; Analytic Imaging Diagnostics Arena (AIDA) initiative - Formas; Analytic Imaging Diagnostics Arena (AIDA) initiative - Swedish Energy Agency; Swiss National Science FoundationSwiss National Science Foundation (SNSF)European Commission [320030_169876, 320030_185028]; Velux Foundation [1123]; Flanders Research FoundationFWO [FWO 12I2121N]</p
Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model
No full textBackground: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones
Detecting anomalies in multivariate time series from automotive systems
No full textThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the automotive industry test drives are conducted during the development of new
vehicle models or as a part of quality assurance for series vehicles. During the test drives, data is recorded for the use of fault analysis resulting in millions of data points. Since multiple vehicles are tested in parallel, the amount of data that is to be analysed is tremendous. Hence, manually analysing each recording is not feasible. Furthermore the complexity of vehicles is ever-increasing leading to an increase of the data volume and complexity of the recordings. Only by effective means of analysing the recordings, one can make sure that the effort put in the conducting of test drives pays off. Consequently, effective means of test drive analysis can become a competitive advantage.
This Thesis researches ways to detect unknown or unmodelled faults in recordings
from test drives with the following two aims: (1) in a data base of recordings, the
expert shall be pointed to potential errors by reporting anomalies, and (2) the time
required for the manual analysis of one recording shall be shortened. The idea to achieve the first aim is to learn the normal behaviour from a training set of recordings and then to autonomously detect anomalies. The one-class classifier “support vector data description” (SVDD) is identified to be most suitable, though it suffers from the need to specify parameters beforehand. One main contribution of this Thesis is a new autonomous parameter tuning approach, making SVDD applicable to the problem at hand. Another vital contribution is a novel approach enhancing SVDD to work with multivariate time series. The outcome is the classifier “SVDDsubseq” that is directly applicable to test drive data, without the need for expert knowledge to configure or tune the classifier. The second aim is achieved by adapting visual data mining techniques to make the manual analysis of test drives more efficient. The methods of “parallel coordinates” and “scatter plot matrices” are enhanced by sophisticated filter and query operations, combined with a query tool that allows to graphically formulate search patterns. As a combination of the autonomous classifier “SVDDsubseq” and user-driven visual data mining techniques, a novel, data-driven, semi-autonomous approach to detect unmodelled faults in recordings from test drives is proposed and successfully validated
on recordings from test drives. The methodologies in this Thesis can be used as a
guideline when setting up an anomaly detection system for own vehicle data
Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease
No full text[EN] The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impairment (MCI). A 3D Convolutional neural network was trained using images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI dataset used for the model training and testing consisted of 822 subjects (472 AD and 350 MCI). The validation was performed on an independent dataset from La Fe University and Polytechnic Hospital. This dataset contained 90 subjects with MCI, 71 of them developed a neurodegenerative disease (64 AD, 4 FTD and 3 DLB) while 19 did not associate any neurodegenerative disease. The model had 79% accuracy, 88% sensitivity and 71% specificity in the identification of patients with neurodegenerative diseases tested on the 10% ADNI dataset, achieving an area under the receiver operating characteristic curve (AUC) of 0.90. On the external validation, the model preserved 80% balanced accuracy, 75% sensitivity, 84% specificity and 0.86 AUC. This binary classifier model based on FDG PET images allows the early prediction of neurodegenerative diseases in MCI patients in standard clinical settings with an overall 80% classification balanced accuracy.This work was financially supported by INBIO 2019 (DEEPBRAIN), INNVA1/2020/83(DEEPPET) funded by Generalitat Valenciana, and PID2019-107790RB-C22 funded by MCIN/AEI/10.13039/501100011033/. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.Prats-Climent, J.; Gandia-Ferrero, MT.; Torres-Espallardo, I.; Álvarez-Sanchez, L.; Martinez-Sanchis, B.; Cháfer-Pericás, C.; Gómez-Rico, I.... (2022). Artificial Intelligence on FDG PET Images Identifies Mild Cognitive
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Post-anaesthesia pulmonary complications after use of muscle relaxants (POPULAR): a multicentre, prospective observational study
No full textBackground Results from retrospective studies suggest that use of neuromuscular blocking agents during general anaesthesia might be linked to postoperative pulmonary complications. We therefore aimed to assess whether the use of neuromuscular blocking agents is associated with postoperative pulmonary complications.Methods We did a multicentre, prospective observational cohort study. Patients were recruited from 211 hospitals in 28 European countries. We included patients (aged >= 18 years) who received general anaesthesia for any in-hospital procedure except cardiac surgery. Patient characteristics, surgical and anaesthetic details, and chart review at discharge were prospectively collected over 2 weeks. Additionally, each patient underwent postoperative physical examination within 3 days of surgery to check for adverse pulmonary events. The study outcome was the incidence of postoperative pulmonary complications from the end of surgery up to postoperative day 28. Logistic regression analyses were adjusted for surgical factors and patients' preoperative physical status, providing adjusted odds ratios (ORadj) and adjusted absolute risk reduction (ARR(adj)). This study is registered with ClinicalTrials. gov, number NCT01865513.Findings Between June 16, 2014, and April 29, 2015, data from 22 803 patients were collected. The use of neuromuscular blocking agents was associated with an increased incidence of postoperative pulmonary complications in patients who had undergone general anaesthesia (1658 [7.6%] of 21 694); ORadj 1.86, 95% CI 1.53-2.26; ARR(adj) -4.4%, 95% CI -5.5 to -3.2). Only 2.3% of high-risk surgical patients and those with adverse respiratory profiles were anaesthetised without neuromuscular blocking agents. The use of neuromuscular monitoring (ORadj 1.31, 95% CI 1.15-1.49; ARR(adj) -2.6%, 95% CI -3.9 to -1.4) and the administration of reversal agents (1.23, 1.07-1.41; -1.9%, -3.2 to -0.7) were not associated with a decreased risk of postoperative pulmonary complications. Neither the choice of sugammadex instead of neostigmine for reversal (ORadj 1.03, 95% CI 0.85-1 center dot 25; ARR(adj) -0.3%, 95% CI -2.4 to 1.5) nor extubation at a train-of-four ratio of 0.9 or more (1.03, 0.82-1.31; -0.4%, -3.5 to 2.2) was associated with better pulmonary outcomes.Interpretation We showed that the use of neuromuscular blocking drugs in general anaesthesia is associated with an increased risk of postoperative pulmonary complications. Anaesthetists must balance the potential benefits of neuromuscular blockade against the increased risk of postoperative pulmonary complications
Post-anaesthesia pulmonary complications after use of muscle relaxants (POPULAR) : a multicentre, prospective observational study
No full textBackground: Results from retrospective studies suggest that use of neuromuscular blocking agents during general anaesthesia might be linked to postoperative pulmonary complications. We therefore aimed to assess whether the use of neuromuscular blocking agents is associated with postoperative pulmonary complications. Methods: We did a multicentre, prospective observational cohort study. Patients were recruited from 211 hospitals in 28 European countries. We included patients (aged ≥18 years) who received general anaesthesia for any in-hospital procedure except cardiac surgery. Patient characteristics, surgical and anaesthetic details, and chart review at discharge were prospectively collected over 2 weeks. Additionally, each patient underwent postoperative physical examination within 3 days of surgery to check for adverse pulmonary events. The study outcome was the incidence of postoperative pulmonary complications from the end of surgery up to postoperative day 28. Logistic regression analyses were adjusted for surgical factors and patients' preoperative physical status, providing adjusted odds ratios (ORadj) and adjusted absolute risk reduction (ARRadj). This study is registered with ClinicalTrials.gov, number NCT01865513. Findings: Between June 16, 2014, and April 29, 2015, data from 22 803 patients were collected. The use of neuromuscular blocking agents was associated with an increased incidence of postoperative pulmonary complications in patients who had undergone general anaesthesia (1658 [7·6%] of 21 694); ORadj 1·86, 95% CI 1·53–2·26; ARRadj −4·4%, 95% CI −5·5 to −3·2). Only 2·3% of high-risk surgical patients and those with adverse respiratory profiles were anaesthetised without neuromuscular blocking agents. The use of neuromuscular monitoring (ORadj 1·31, 95% CI 1·15–1·49; ARRadj −2·6%, 95% CI −3·9 to −1·4) and the administration of reversal agents (1·23, 1·07–1·41; −1·9%, −3·2 to −0·7) were not associated with a decreased risk of postoperative pulmonary complications. Neither the choice of sugammadex instead of neostigmine for reversal (ORadj 1·03, 95% CI 0·85–1·25; ARRadj −0·3%, 95% CI −2·4 to 1·5) nor extubation at a train-of-four ratio of 0·9 or more (1·03, 0·82–1·31; −0·4%, −3·5 to 2·2) was associated with better pulmonary outcomes. Interpretation: We showed that the use of neuromuscular blocking drugs in general anaesthesia is associated with an increased risk of postoperative pulmonary complications. Anaesthetists must balance the potential benefits of neuromuscular blockade against the increased risk of postoperative pulmonary complications. Funding: European Society of Anaesthesiology
Post-anaesthesia pulmonary complications after use of muscle relaxants (POPULAR): A multicentre, prospective observational study
No full textBackground: Results from retrospective studies suggest that use of neuromuscular blocking agents during general anaesthesia might be linked to postoperative pulmonary complications. We therefore aimed to assess whether the use of neuromuscular blocking agents is associated with postoperative pulmonary complications. Methods: We did a multicentre, prospective observational cohort study. Patients were recruited from 211 hospitals in 28 European countries. We included patients (aged ≥18 years) who received general anaesthesia for any in-hospital procedure except cardiac surgery. Patient characteristics, surgical and anaesthetic details, and chart review at discharge were prospectively collected over 2 weeks. Additionally, each patient underwent postoperative physical examination within 3 days of surgery to check for adverse pulmonary events. The study outcome was the incidence of postoperative pulmonary complications from the end of surgery up to postoperative day 28. Logistic regression analyses were adjusted for surgical factors and patients' preoperative physical status, providing adjusted odds ratios (ORadj) and adjusted absolute risk reduction (ARRadj). This study is registered with ClinicalTrials.gov, number NCT01865513. Findings: Between June 16, 2014, and April 29, 2015, data from 22 803 patients were collected. The use of neuromuscular blocking agents was associated with an increased incidence of postoperative pulmonary complications in patients who had undergone general anaesthesia (1658 [7·6%] of 21 694); ORadj 1·86, 95% CI 1·53–2·26; ARRadj −4·4%, 95% CI −5·5 to −3·2). Only 2·3% of high-risk surgical patients and those with adverse respiratory profiles were anaesthetised without neuromuscular blocking agents. The use of neuromuscular monitoring (ORadj 1·31, 95% CI 1·15–1·49; ARRadj −2·6%, 95% CI −3·9 to −1·4) and the administration of reversal agents (1·23, 1·07–1·41; −1·9%, −3·2 to −0·7) were not associated with a decreased risk of postoperative pulmonary complications. Neither the choice of sugammadex instead of neostigmine for reversal (ORadj 1·03, 95% CI 0·85–1·25; ARRadj −0·3%, 95% CI −2·4 to 1·5) nor extubation at a train-of-four ratio of 0·9 or more (1·03, 0·82–1·31; −0·4%, −3·5 to 2·2) was associated with better pulmonary outcomes. Interpretation: We showed that the use of neuromuscular blocking drugs in general anaesthesia is associated with an increased risk of postoperative pulmonary complications. Anaesthetists must balance the potential benefits of neuromuscular blockade against the increased risk of postoperative pulmonary complications. Funding: European Society of Anaesthesiology
Evaluation of unsupervised grouping strategies in the determination of patterns associated with failures of thermal systems in bulk trucks
No full textilustraciones, diagramas, gráficas, tablasEsta tesis tiene como finalidad evaluar estrategias de agrupamiento no supervisadas para datos asociados a tractocamiones graneleros, en la detección de patrones de falla en sistemas térmicos. El estudio de estas técnicas es importante en el ámbito del mantenimiento predictivo basado en datos con la implementación de algoritmos de aprendizaje de máquinas que permitan planificar adecuadamente cronogramas de mantenimiento en empresas de transporte de carga. Para el desarrollo de la tesis, se usa como fuente de información los dispositivos de telemetría de los tractocamiones graneleros de una empresa colombiana de transporte de carga que reportan datos en tiempo real de la medición de variables como velocidad, temperaturas, estado de operación del vehículo, entre otras para el año 2020. También se usa el histórico de ingresos a taller de la flota de 116 tractocamiones donde se analizan los ingresos a taller para la intervención de sistemas térmicos. Estos datos son el insumo para la evaluación de las estrategias de agrupamiento propuestas en este trabajo. Los resultados parten desde la obtención de los datos, preparación de estos y análisis descriptivos para implementar técnicas de reducción de dimensionalidad en la información y posteriormente evaluar el comportamiento de algoritmos de agrupamiento para la detección de patrones de falla que se relacionen a daños en sistemas térmicos de los vehículos. Con el desarrollo de este trabajo se encuentra un potencial para el ahorro en costos correctivos de la flota en taller que apunte a una adecuada gestión de la flota en modelos de pago por uso, apalancando la disponibilidad de los vehículos en las operaciones de transporte. (Texto tomado de la fuente)The purpose of this thesis is to evaluate unsupervised clustering strategies for data associated with bulk carrier trucks, in the detection of failure patterns in thermal systems. The study of these techniques is important in the field of data-based predictive maintenance with the implementation of machine learning algorithms that allow proper planning of maintenance schedules in freight transport companies. For the development of the thesis, the telemetry devices of the bulk tractor trucks of a Colombian cargo transport company are used as a source of information, which report data in real time of the measurement of variables such as speed, temperatures, state of operation of the vehicle, among others for the year 2020. The history of workshop entries of the fleet of 116 tractor-trailers is also used, where workshop entries for the intervention of thermal systems are analyzed. These data are the input for the evaluation of the grouping strategies proposed in this work. The results start from obtaining the data, preparing them and descriptive analysis to implement dimensionality reduction techniques in the information and subsequently evaluate the behavior of grouping algorithms for the detection of failure patterns that are related to damage in thermal systems. With the development of this work, there is a potential for savings in corrective costs of the fleet in the workshop that points to an adequate management of the fleet in pay-per-use models, leveraging the availability of vehicles in transport operations.MaestríaMagister en Ingeniería - AnalíticaMantenimiento predictivoAnálisis de datosÁrea Curricular de Ingeniería de Sistemas e Informátic
