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Personalized therapy using drug delivery devices 12

Personalized therapy using drug delivery devices

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Drexler, Daniel A. Gergics, Borbala Puskas, Melania Haidegger, Tanias P. Kovacs, Levente A. Obuda Univ Univ Res & Innovat Ctr EKIK Physiol Controls Res Ctr Budapest Hungary Obuda Univ EKIK Antal Bejczy Ctr Intelligent Robot Budapest Hungary ACMIT Austrian Ctr Med Innovat & Technol Wiener Neustadt Austria

Automated Drug Delivery (ADD) systems refer to advanced medical technologies and methods designed to transfer pharmaceutical compounds to specific sites within the body in an automated, controlled, precise, and efficient manner. These systems aim to reduce side effects, prevent over-dosage, and improve patient adherence to treatment regimens;moreover, they bring relaxation to healthcare providers through automated quality assurance. ADD systems have become demanding components in achieving personalized therapy and precision medicine. Technology currently allows to automatically medicate people with portable devices. Drug delivery devices are available, but the sensors need further development and testing to enable safe and more accurate measurements in the future. Exploiting the potential in ADD systems requires the application of control theory. This paper reviews the currently available drug delivery devices and examines how our existing control algorithms may enhance a wearable system. We discuss algorithms for both impulsive therapy and continuous therapy, covering the possible applications. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： drug delivery systems wearable devices control algorithms personalized therapy

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Statistical methods to evaluate discrete Boolean mathematical models from systems biology experimental data sets 26

Statistical methods to evaluate discrete Boolean mathematica...

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26th International symposium on Mathematical Theory of Networks and systems (MTNS)

作者： Bensussen, Antonio Arciniega-Gonzalez, J. Arturo Pacheco-Marin, Rosario Alvarez-Buylla, Elena R. Carlos Martinez-Garcia, Juan Cinvestav IPN Dept Control Automat Mexico City DF Mexico Univ Nacl Autonoma Mexico Posgrad Ciencias Biomed Mexico City DF Mexico Univ Nacl Autonoma Mexico C3 Mexico City DF Mexico CONAHCYT Mexico City DF Mexico Univ Nacl Autonoma Mexico Inst Ecol Mexico City DF Mexico

Discrete Boolean models offer qualitative insights into gene regulatory networks, enhancing understanding of cellular phenotypes. Despite lacking quantitative parameters, their reliability stems from data derived through systematic reviews and databases supported by experimental validation. Unlike other mathematical models, they don't employ statistical tests for intrinsic validity, making it challenging to establish their utility in interpreting experimental data. In this paper, we propose two statistical procedures to validate different description levels obtained from Boolean models, addressing simulated mutations and stationary states. These methods provide guidelines for integrating data from massive sequencing techniques, such as RNA-seq. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： systems Biology Networked systems biomedical System Modeling Bioinformatics

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Comparative Virtual Trials: Pressure-controlled versus Volume-controlled Stochastic Integrated Model-Based Mechanical Ventilation Protocols 12

Comparative Virtual Trials: Pressure-Controlled versus Volum...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Ang, Christopher Yew Shuen Chiew, Yeong Shiong Wang, Xin Ooi, Ean Hin Nor, Mohd Basri Mat Zhou, Cong Chase, J. Geoffrey Monash Univ Malaysia Sch Engn Selangor Malaysia Int Islamic Univ Malaysia Kulliyah Med Kuantan 25200 Malaysia Univ Canterbury Ctr Bioengn Christchurch New Zealand

Pressure-controlled (PC) ventilation is a pressure-targeted mode of mechanical ventilation (MV) to prevent barotrauma by using pre-set driving pressures. However, the lack of clinician consensus on approaches towards pressure-targeted MV management can lead to suboptimal MV treatment and adverse patient outcomes. This study presents an in-silico trial for the validation of a pressure-based, stochastic model-integrated SiVENT protocol to guide this care. The PC SiVENT protocol uses a respiratory elastance (Ers) stochastic model and model-based methods to recommend a safe combination of MV input settings. The protocol was implemented on PC virtual patients (VP) (N=29;2,630 hours MV data) in a virtual trial to derive protocol-suggested MV settings and resulting VP responses to these MV settings. Virtual trial outcomes were compared with the retrospective clinical data and similar volume-control (VC) SiVENT virtual trials, by assessing the compliance rate of measured VP responses to established clinical safety thresholds. Out of 29 PC VPs, 3 retrospective VPs (similar to 10%) and 23 SiVENT VPs (similar to 79%) achieve an all compliance rate of > 80%. The results of the virtual trials demonstrate the efficacy of the SiVENT PC in regulating pressure-based MV outcomes, as well as the potential to safely streamline the development, optimisation and validation process of new MV decision support systems. The protocols presented would make measurable advances in individualised care with significant potential following improved patient outcomes in MV. The overall results justify prospective clinical trials. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Mechanical Ventilation Pressure control Virtual Patient Model-Based Protocols Virtual Trials

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Identifying EEG-based Functional Networks for Whole-Brain Models 11

Identifying EEG-based Functional Networks for Whole-Brain Mo...

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11th Vienna International Conference on Mathematical modelling (MATHMOD)

作者： Edthofer, Alexander Koerner, Andreas TU Wien Inst Anal & Sci Comp Wiedner Hauptstr 8-10 A-1040 Vienna Austria

Brain activity differs according to the state of consciousness. Whole-brain models, typically based on functional magnetic resonance imaging (fMRI) data, provide valuable insight into these changes by utilizing structural connectivity and differential equations to model the functional connectivity between brain regions. The goal of this work is to adapt fMRI-based functional connectivity models to electroencephalography data. A key step in this process is to determine the number of clusters and to estimate the coupling parameters. We turn to amplitude envelope correlation, a time-domain measure, to better match functional connectivity patterns observed in fMRI. By analyzing 64-channel electroencephalogram data from 25 male subjects over the age of 60 from the AlphaMax study, we investigate wakefulness and the transition to unconsciousness under anesthesia. Using k-means clustering, we identify optimal brain network configurations, focusing on whether they match known fMRI-based networks. Clustering is evaluated using the Calinski-Harabasz criterion for different thresholds and numbers of cluster. The results show that two clusters are predominantly optimal for both awake and the mixed half awake, half unconscious scenario. Misplaced electrodes are mainly found in parietal regions. Since we determined the number of differential equations, this work lays the foundation for further development of electroencephalography-based whole-brain models that can track functional connectivity changes during anesthesia. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Electroencephalogram Functional Connectivity Amplitude Envelope Correlation Whole-Brain Model Monitoring biomedical System Modeling Neuro-systems Clustering

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Geometric Data Fusion for Collaborative Attitude Estimation 26

Geometric Data Fusion for Collaborative Attitude Estimation

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26th International symposium on Mathematical Theory of Networks and systems (MTNS)

作者： Ge, Yixiao Zamani, Behzad van Goor, Pieter Trumpf, Jochen Mahony, Robert Australian Natl Univ Sch Engn Canberra ACT Australia Univ Melbourne Dept Elect & Elect Engn Melbourne Vic Australia

In this paper, we consider the collaborative attitude estimation problem for a multi-agent system. The agents are equipped with sensors that provide directional measurements and relative attitude measurements. We present a bottom-up approach where each agent runs an extended Kalman filter (EKF) locally using directional measurements and augments this with relative attitude measurements provided by neighbouring agents. The covariance estimates of the relative attitude measurements are geometrically corrected to compensate for relative attitude between the agent that makes the measurement and the agent that uses the measurement before being fused with the local estimate using the convex combination ellipsoid (CCE) method to avoid data incest. Simulations are undertaken to numerically evaluate the performance of the proposed algorithm. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： data fusion geometric approaches stochastic modelling fusion covariance matrices

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In-Silico Validation of Insulin Sensitivity Prediction by Neural Network-based Quantile Regression 12

In-Silico Validation of Insulin Sensitivity Prediction by Ne...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Alkhafaf, Omer S. Alsultani, Ameer B. Roel, Alaa N. Szabo, Balint Pintar, Petra Szlavecz, Akos Palancz, Bela Kovacs, Katalin Chase, J. Geoffrey Benyo, Balazs Budapest Univ Tech & Eco Fac Elect Engn & Informat Tech Dept Control Engn & Informat Tech Budapest Hungary Univ Canterbury Dept Mech Engn Christchurch New Zealand Szechenyi Istvan Univ Fac Informat & Elect Engn Dept Informat Gyor Hungary Univ Mosul Fac Dent Mosul Iraq Semmelweis Univ Fac Dent Dept Oral Diagnost Budapest Hungary

High blood glucose levels and stress-induced hyperglycemia are common issues in intensive care units (ICU). controlling blood glucose levels is crucial but challenging due to patient-specific variability. This challenge was addressed by developing model-based control protocols, which rely on identifying and predicting the patient-specific metabolic state. This study presents the in-silico simulation-based evaluation of a new artificial neural network-based insulin sensitivity (SI) prediction method. The models were trained on a dataset collected during clinical treatment using the stochastic-targeted (STAR) protocol and evaluated by simulating the clinical interventions on virtual patients created from retrospective clinical data. The results show the new models could be safely applied for SI prediction. Furthermore, the adopted method had very similar accuracy in the overall comparison of cohorts, with only minor differences noted in hypoglycemia events. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Quantile Regression Insulin Sensitivity Glycaemic control Intensive Care Unit In-silico patients

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Improved control System for Digital Imaging Elasto-Tomography Breast Cancer Screening 12

Improved Control System for Digital Imaging Elasto-Tomograph...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Hall, Henry W. Fitzjohn, Jessica Zhou, Cong Chase, J. Geoffrey Univ Canterbury Ctr Bioengn Dept Mech Engn Christchurch New Zealand

Breast cancer is the most recognised form of cancer in New Zealand women. Early treatment is a key factor in the long-term outcome of the disease. Robust screening methods are an essential component to help early diagnoses. Common screening methods include mammography and physical examination Both methods have significant downsides including discomfort and exposure to radiation. Digital Imaging Elasto-Tomography (DIET) is a new breast cancer screening technology. Screening involves women lying prone on the machine, while sinewaves of known amplitude and frequency are applied to the breast at the nipple. Images are taken at multiple phases of the sinewave as the breast is vibrated. These images are processed to determine the surface motion and consequently the tissue properties of the breast, which is then used to distinguish tumours from healthy tissue. A prototype of this machine has been designed and used on breast phantoms and in clinical trials. A known issue with the prototype is noise in the actuation system. This creates vibration in the rest of the machine and impacts the quality of the oscillation imparted to the breast. A possible solution isolates the actuator from the machine by using damping feet. The motion of the feet was measured using an extra Linear Variable Differential Transformer (LVDT) sensor. Feedback control for the actuation was provided by the combination of this sensor and the original one. The results of these changes were compared to using a single sensor for feedback. The vibration of the feet reduced the overall amplitude of the actuator when in operation. This reduction was accounted for when two sensors were used for feedback. The Signal to Noise Ratio (SNR) was used to quantify the quality of the actuation. At 20 Hz the SNR was 22.7 d beta and 22.0 d beta using one and two sensors respectively. At 50 Hz the SNR improved to 39.9 d beta and 38.6 d beta for one and two sensors respectively. The impact of the reduced range of motion cou

关键词： Breast Cancer Mammography Digital Imaging Elasto-Tomography Non-Invasive control

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Cardiovascular Model Identification Using Neural ODE 12

Cardiovascular Model Identification Using Neural ODE

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Szabo, Balint Antal, Akos Szlavecz, Akos Palancz, Bela Kovacs, Katalin Murphy, Liam Cushway, James Davey, Nicolas Zhou, Cong Chase, J. Geoffrey Benyo, Balazs Budapest Univ Technol & Econ Fac Elect Engn & Informat Dept Control Engn & Informat Technol Budapest Hungary Semmelweis Univ Fac Dent Dept Oral Diagnost Budapest Hungary Univ Canterbury Christchurch New Zealand Szechenyi Istvan Univ Fac Mech Engn Informat & Elect Engn Dept Informat Gyor Hungary Univ Liege ULg GIGA Silico Med Liege Belgium Budapest Univ Technol & Econ Fac Civil Engn Dept Geodesy & Surveying Budapest Hungary

Acute circulatory failure (ACF) is a clinical syndrome when the heart and circulatory circulation cannot provide adequate blood supply to meet metabolic needs of the organs. ACF affects 30%- 50% of intensive care unit (ICU) patients. Fluid resuscitation is the primary treatment of ACF. However, it fails in a significant proportion (about 50%) of cases due to lack of clinically feasible non-invasive perfusion markers to assess the efficacy of the fluid therapy. Unfortunately, unsuccessful fluid therapy negatively affects patient outcome, increasing ICU length of stay and costs. Recent studies show identifying Stressed Blood Volume (SBV) of the cardiovascular system can be used to assess the potential efficacy of fluid therapy. The development of the diagnostic method requires the identification of the central arterial pressure curve based on the femoral arterial pressure, which is clinically available. This central arterial pressure curve can be used to identify the cardiovascular system parameters. In this study, the main goal was to develop a parameter-identification method for the Tube-load model-based transfer function connecting the femoral and central arterial pressure curve by using the so-called Physics-informed Neural Network methodology, namely the Neural ODE method. The study presents the adaptation of the Neural ODE method to the given parameter identification problem and the validation of the developed identification method. The robustness of the developed identification method was tested and used on a series of measurement data recorded in animal experiments. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： personalized model-based medical therapy methods cardiovascular system physiological system modelling parameter identification transfer function Neural ODE intensive care

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Virtual clinical trials for mechanically ventilated respiratory failure patients under volume-controlled ventilation - The Challenges of Volume-control Protocols 12

Virtual clinical trials for mechanically ventilated respirat...

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12th ifac symposium on Biological and Medical systems (BMS)

Resource-intensive clinical trial is a crucial step in validating the safety and effectiveness of personalised, model-based mechanical ventilation (MV) treatments prior to implementation as a clinical standard of care. An alternative first step would be validated in-silico virtual patient (VP) trials. This study evaluates the efficacy of two model-based MV protocols (MBP) in regulating key pressure-based MV outcomes in volume-controlled MV using a well-established in-silico, virtual trial methodology. Two modified, closed-loop MBPs were implemented on volume-controlled VPs (N=18;955 hours MV data) to derive protocol-suggested MV settings and resulting VP responses to these MV settings. Virtual trial outcomes were compared between the two protocols, and with the retrospective clinical data, by assessing the adherence of measured VP responses to established clinical safety thresholds. A successful intervention is defined as an intervention interval during which the protocol successfully recommended a safe combination of MV settings, defined by established clinical safety thresholds. Both protocols performed 637 interventions with a success rate of 93.72%, improving the overall adherence of measured VP responses to established clinical safety thresholds by 53% compared to the retrospective clinical data. This virtual trial further validates the efficacy of VPs for long-term safety and performance assessments of MBPs. They also demonstrate the potential to safely streamline the development, optimisation and validation process of new MV decision support systems, as has been done in other areas of care. The overall outcomes could ultimately accelerate opportunities for the successful translation of these systems into clinical practice. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Mechanical Ventilation Virtual Patient Model-Based Protocols Virtual Trials

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13th ifac symposium on Robot control, SYROCO 2022 - Proceedings

13th IFAC Symposium on Robot Control, SYROCO 2022 - Proceedi...

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13th ifac symposium on Robot control, SYROCO 2022

ISBN: (纸本)9781713867890

The proceedings contain 31 papers. The topics discussed include: preference-based optimization of a human-robot collaborative controller;kinematic modelling and design of a tendon actuated soft manipulator;control of an orbital manipulator with reaction wheels for on-orbit servicing;sampling-based trajectory (re)planning for differentially flat systems: application to a 3D gantry crane;attitude control in the mini cheetah robot via MPC and reward-based feed-forward controller;stiffness optimized multi-robot behavior planning using reduced hessian method;control of a three-degree-of-freedom variable inertia generator;control of a robot expressive movements using non-verbal features;analysis and design of a macro-mini robotic system for physical human-robot interaction;whole-body control of a mobile manipulator for passive collaborative transportation;a cognitive architecture for robot-assisted surgical procedures;and interval-valued invehicular latency characterization for risk/fault management purposes.

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