TY - CHAP AU - Al-sultani Ameer Basheer Yousif, Ameer AU - Omer, Alkhafaf AU - J., Geoffrey Chase AU - Benyó, Balázs István TI - Design a Stepped Impedance Resonator Microstrip Filter for Glucose Concentration Detection T2 - Proceedings of the Workshop on the Advances of Information Technology 2024 PB - BME Irányítástechnika és Informatika Tanszék CY - Budapest SN - 9789634219422 PY - 2024 SP - 165 EP - 169 PG - 5 UR - https://m2.mtmt.hu/api/publication/34597096 ID - 34597096 LA - English DB - MTMT ER - TY - CHAP AU - Alaa, N. Roel AU - Omer, Alkhafaf AU - Al-sultani Ameer Basheer Yousif, Ameer AU - Szlávecz, Ákos József AU - Paláncz, Béla AU - Báint, Szabó AU - Cong, Zhou AU - J., Geoffrey Chase AU - Benyó, Balázs István TI - Prediction of Insulin Sensitivity Using Neural Network based Quantile Regression: In-Silico Validation T2 - Proceedings of the Workshop on the Advances of Information Technology 2024 PB - BME Irányítástechnika és Informatika Tanszék CY - Budapest SN - 9789634219422 PY - 2024 SP - 186 EP - 192 PG - 7 UR - https://m2.mtmt.hu/api/publication/34597082 ID - 34597082 LA - English DB - MTMT ER - TY - CHAP AU - Omer, Alkhafaf AU - Al-sultani Ameer Basheer Yousif, Ameer AU - Szlávecz, Ákos József AU - Alaa, N. Roel AU - Paláncz, Béla AU - Báint, Szabó AU - Cong, Zhou AU - J., Geoffrey Chase AU - Benyó, Balázs István TI - Quantile Regression Based Prediction of Insulin Sensitivity Using Neural Network Model for Better Glycaemic Control in Intensive Care T2 - Proceedings of the Workshop on the Advances of Information Technology 2024 PB - BME Irányítástechnika és Informatika Tanszék CY - Budapest SN - 9789634219422 PY - 2024 SP - 176 EP - 185 PG - 10 UR - https://m2.mtmt.hu/api/publication/34597041 ID - 34597041 LA - English DB - MTMT ER - TY - JOUR AU - Szabó, Bálint AU - Szlávecz, Ákos József AU - Bazsó, Sándor AU - Somogyi, Péter AU - Kovács, Katalin AU - Viola, Árpád AU - Benyó, Balázs István TI - Modeling the Correlation of Human Vertebral Body Volumes JF - IFAC PAPERSONLINE J2 - IFACOL VL - 56 PY - 2023 IS - 2 SP - 9030 EP - 9035 PG - 6 SN - 2405-8971 DO - 10.1016/j.ifacol.2023.10.133 UR - https://m2.mtmt.hu/api/publication/34410706 ID - 34410706 N1 - Azbil Corporation; et al.; Fujita Corporation; Hitachi, Ltd.; Kumagai Gumi Co., Ltd.; The Society of Instrument and Control Engineers (SICE) Budapest University of Technology and Economics, Faculty of Electrical Engineering and Information Technology, Department of Control Engineering and Information Technology, Budapest, Hungary Department of Informatics, Széchenyi István University, Gyor, Hungary Péterfy Sándor Street Hospital, Department of Neurosurgery, Budapest, Hungary Conference code: 195861 Export Date: 23 February 2024 Correspondence Address: Szabó, B.; Budapest University of Technology and Economics, Hungary Correspondence Address: Szlávecz, Á.; Budapest University of Technology and Economics, Hungary Correspondence Address: Bazsó, S.; Budapest University of Technology and Economics, Hungary Correspondence Address: Somogyi, P.; Budapest University of Technology and Economics, Hungary Correspondence Address: Benyó, B.I.; Budapest University of Technology and Economics, Hungary Funding details: Horizon 2020 Framework Programme, H2020, 872488 Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA, OTKA K137995, TKP2021-EGA-02 Funding text 1: This research was supported by the National Research, Development, and Innovation Fund, Hungary, Grant No. OTKA K137995, TKP2021-EGA-02, and by H2020 MSCA-RISE DCPM (GA ID: 872488) grant. The authors would like to express their thanks for their contribution to the image segmentation for Diána Korösi, Dóra Karolina Kapui, Andrea Mihail, Csanád Hende, and Zoltán Vatai. AB - Anatomical parameters of the human body strongly correlate with each other. Modelling these dependencies enables the creation of a realistic anatomical human body model that can be parameterized. Such a model can be used for several diagnostic processes to identify abnormalities or even give guidance in surgical interventions. This paper proposes a probabilistic model describing the dependencies between the vertebral body volumes of humans from the Caucasian human race. As demonstrated, the proposed model can accurately describe the relationship between the vertebral body volumes and is used for the prediction of an unknown vertebral volume based on a known one. The probabilistic model is created by using the CT segmentation of 37 patients. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) LA - English DB - MTMT ER - TY - JOUR AU - Stein, Erik AU - Chen, Rongqing AU - Battistel, Alberto AU - Lovas, András AU - Benyó, Balázs István AU - Möller, Knut TI - Influence of Reconstruction Algorithms on Harmonic Analysis in Electrical Impedance Tomography JF - IFAC PAPERSONLINE J2 - IFACOL VL - 56 PY - 2023 IS - 2 SP - 5615 EP - 5619 PG - 5 SN - 2405-8971 DO - 10.1016/j.ifacol.2023.10.469 UR - https://m2.mtmt.hu/api/publication/34410689 ID - 34410689 N1 - Azbil Corporation; et al.; Fujita Corporation; Hitachi, Ltd.; Kumagai Gumi Co., Ltd.; The Society of Instrument and Control Engineers (SICE) Institute of Technical Medicine (ITeM), Furtwangen University (HFU), Villingen-Schwenningen, Germany Faculty of Engineering, University of Freiburg, Freiburg, Germany Department of Anaesthesiology and Intensive Therapy, Kiskunhalas Semmelweis Hospital, Kiskunhalas, Hungary Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics, Department of Control Engineering and Information Technology, Budapest, Hungary Conference code: 195861 Export Date: 9 February 2024 Correspondence Address: Stein, E.; Institute of Technical Medicine (ITeM), Germany; email: e.stein@hs-furtwangen.de Correspondence Address: Chen, R.; Institute of Technical Medicine (ITeM), Germany Correspondence Address: Battistel, A.; Institute of Technical Medicine (ITeM), Germany Correspondence Address: Möller, K.; Institute of Technical Medicine (ITeM), Germany Funding details: Bundesministerium für Bildung und Forschung, BMBF Funding details: Markets, Organizations and Votes in Economics, MOVE, 13FH628IX6, 32-7545.220/42/1 Funding text 1: Research funding: This research was partially supported by the German Federal Ministry of Education and Research (MOVE, Grant 13FH628IX6) and the grant AIRLobe (32-7545.220/42/1) funded by”Innovative Projects” MWK-BW AB - Electrical Impedance Tomography (EIT) is a commonly used imaging technique for monitoring respiration on the bedside and it might have the potential for monitoring lung perfusion. Several signal processing approaches have been developed to separate respiration and perfusion. In this contribution we investigated whether different image reconstruction algorithms influence the separation results provided by the harmonic analysis approach. We compared the algorithms used by Dräger, the Gauss-Newton method with different regularizers as well as the GREIT algorithm. The comparison was carried out using a retrospective EIT dataset from a COVID-19 patient. The results gave insight that the harmonic analysis separation approach is dependent on the reconstruction algorithms. Both, the separation of the perfusion and the separation of the respiration showed differences between the reconstruction algorithms when carried out pixel-wise. On the other hand, the separations carried out on the global impedance only showed marginal differences for the separated perfusion. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) LA - English DB - MTMT ER - TY - JOUR AU - Szabó, Bálint AU - Szlávecz, Ákos József AU - Paláncz, Béla AU - Kovács, Katalin AU - Chase, J. Geoffrey AU - Benyó, Balázs István TI - Comparison of Three Artificial Intelligence Methods for Predicting 90% Quantile Interval of Future Insulin Sensitivity of Intensive Care Patients JF - IFAC PAPERSONLINE J2 - IFACOL VL - 56 PY - 2023 IS - 2 SP - 2091 EP - 2095 PG - 5 SN - 2405-8971 DO - 10.1016/j.ifacol.2023.10.1110 UR - https://m2.mtmt.hu/api/publication/34410684 ID - 34410684 AB - Three alternative artificial intelligence-based insulin sensitivity prediction methods are compared in this study. Insulin sensitivity prediction is an essential step in calculating the optimal treatment options in model-based glycemic control protocol of insulin-dependent intensive care patients. The prediction methods must predict not only the expected value of the insulin sensitivity for a given time horizon but also the 90% confidence interval making the prediction problem more specific compared to the common prediction problems. All of the proposed prediction methods - proposed in our previous publications - use different neural network models: a classification deep neural network model, a Mixture Density Network based model, and a Quantile regression based model. The patent data set used for the development and accuracy assessment is from 3 clinical ICU cohorts, including 820 treatment episodes of 606 patients and 68,631 hours of treatment. To evaluate the efficacy of the prediction in the context of clinical requirements, three metrics are used Success rate, Interval ratio, and I-Score are applied. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) LA - English DB - MTMT ER - TY - CHAP AU - Geoffrey Chase, J. AU - Cong, Zhou AU - Jennifer, L. Knopp AU - Knut, Moeller AU - Benyó, Balázs István AU - Thomas, Desaive AU - Jennifer, H. K. Wong AU - Sanna, Malinen AU - Katharina, Naswall AU - Geoffrey, M. Shaw AU - Bernard, Lambermont AU - Yeong, S. Chiew TI - Digital Twins and Automation of Care in the Intensive Care Unit T2 - Cyber-Physical-Human Systems: Fundamentals and Applications PB - IEEE PY - 2023 SP - 457 EP - 489 PG - 33 DO - 10.1002/9781119857433.ch17 UR - https://m2.mtmt.hu/api/publication/34045897 ID - 34045897 N1 - Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand School of Civil Aviation, Northwestern Polytechnical University, Taicang, China Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary GIGA In Silico Medicine, Liege University, Liege, Belgium Department of Management, Marketing, and Entrepreneurship, University of Canterbury, Christchurch, New Zealand School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand Department of Intensive Care, CHU de Liege, Liege, Belgium Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, Selangor, Malaysia Export Date: 27 July 2023 LA - English DB - MTMT ER - TY - JOUR AU - Benyó, Balázs István AU - Paláncz, Béla AU - Szlávecz, Ákos József AU - Szabó, Bálint AU - Kovács, Katalin AU - Chase, J. Geoffrey TI - Classification-based Deep Neural Network vs Mixture Density Network Models for Insulin Sensitivity Prediction Problem JF - COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE J2 - COMPUT METH PROG BIO VL - 240 PY - 2023 PG - 9 SN - 0169-2607 DO - 10.1016/j.cmpb.2023.107633 UR - https://m2.mtmt.hu/api/publication/34012024 ID - 34012024 N1 - Funding Agency and Grant Number: National Research, Development, and Innovation Office, Hungary [K116574, K137995]; [872488] Funding text: The research was supported by the National Research, Development, and Innovation Office, Hungary , (Grant No. K116574 , K137995 , TKP2021-EGA-02, and by H2020 MSCA-RISE DCPM (GA ID: 872488) ) grant. AB - Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol - used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia - is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classi-fication deep neural network and a Mixture Density Network based method. Treatment data from three different patient cohorts are used to train the network models. Accuracy of neural network predictions are compared with the current model-based predictions used to guide care. The prediction accuracy was found to be the same or better than the reference. The authors suggest that these methods may be a promising alternative in model-based clinical treatment for patient state prediction. Still, more research is needed to validate these findings, including in-silico simulations and clinical validation trials. & COPY; 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) LA - English DB - MTMT ER - TY - JOUR AU - Chen, Rongqing AU - Krueger-Ziolek, Sabine AU - Lovas, András AU - Benyó, Balázs István AU - Rupitsch, Stefan J. AU - Moeller, Knut TI - Structural priors represented by discrete cosine transform improve EIT functional imaging JF - PLOS ONE J2 - PLOS ONE VL - 18 PY - 2023 IS - 5 PG - 24 SN - 1932-6203 DO - 10.1371/journal.pone.0285619 UR - https://m2.mtmt.hu/api/publication/33829896 ID - 33829896 N1 - Institute of Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany Faculty of Engineering, University of Freiburg, Freiburg, Germany Department of Anaesthesiology and Intensive Therapy, Kiskunhalas Semmelweis Hospital, Kiskunhalas, Hungary Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary Export Date: 1 June 2023 CODEN: POLNC Correspondence Address: Chen, R.; Institute of Technical Medicine (ITeM), Germany; email: rongqing.chen@hs-furtwangen.de AB - Structural prior information can improve electrical impedance tomography (EIT) reconstruction. In this contribution, we introduce a discrete cosine transformation-based (DCT-based) EIT reconstruction algorithm to demonstrate a way to incorporate the structural prior with the EIT reconstruction process. Structural prior information is obtained from other available imaging methods, e.g., thorax-CT. The DCT-based approach creates a functional EIT image of regional lung ventilation while preserving the introduced structural information. This leads to an easier interpretation in clinical settings while maintaining the advantages of EIT in terms of bedside monitoring during mechanical ventilation. Structural priors introduced in the DCT-based approach are of two categories in terms of different levels of information included: a contour prior only differentiates lung and non-lung region, while a detail prior includes information, such as atelectasis, within the lung area. To demonstrate the increased interpretability of the EIT image through structural prior in the DCT-based approach, the DCT-based reconstructions were compared with reconstructions from a widely applied one-step Gauss-Newton solver with background prior and from the advanced GREIT algorithm. The comparisons were conducted both on simulation data and retrospective patient data. In the simulation, we used two sets of forward models to simulate different lung conditions. A contour prior and a detail prior were derived from simulation ground truth. With these two structural priors, the reconstructions from the DCT-based approach were compared with the reconstructions from both the one-step Gauss-Newton solver and the GREIT. The difference between the reconstructions and the simulation ground truth is calculated by the ℓ 2 -norm image difference. In retrospective patient data analysis, datasets from six lung disease patients were included. For each patient, a detail prior was derived from the patient’s CT, respectively. The detail prior was used for the reconstructions using the DCT-based approach, which was compared with the reconstructions from the GREIT. The reconstructions from the DCT-based approach are more comprehensive and interpretable in terms of preserving the structure specified by the priors, both in simulation and retrospective patient data analysis. In simulation analysis, the ℓ 2 -norm image difference of the DCT-based approach with a contour prior decreased on average by 34% from GREIT and 49% from the Gauss-Newton solver with background prior; for reconstructions of the DCT-based approach with detail prior, on average the ℓ 2 -norm image difference is 53% less than GREIT and 63% less than the reconstruction with background prior. In retrospective patient data analysis, the reconstructions from both the DCT-based approach and GREIT can indicate the current patient status, but the DCT-based approach yields more interpretable results. However, it is worth noting that the preserved structure in the DCT-based approach is derived from another imaging method, not from the EIT measurement. If the structural prior is outdated or wrong, the result might be misleadingly interpreted, which induces false clinical conclusions. Further research in terms of evaluating the validity of the structural prior and detecting the outdated prior is necessary. LA - English DB - MTMT ER - TY - CHAP AU - Szabó, Bálint AU - Omer, Alkhafaf AU - Al-sultani Ameer Basheer Yousif, Ameer AU - P., Pintér AU - Szlávecz, Ákos József AU - Paláncz, Béla AU - J., Geoffrey Chase AU - Benyó, Balázs István TI - Comparison of Neural Network Models for Quantile Regression Based Insulin Sensitivity Prediction T2 - Proceedings of the Workshop on the Advances of Information Technology 2023 PB - BME Irányítástechnika és Informatika Tanszék CY - Budapest SN - 9789634218968 PY - 2023 SP - 1 EP - 6 PG - 6 UR - https://m2.mtmt.hu/api/publication/33704875 ID - 33704875 LA - English DB - MTMT ER -