Design and Demonstration of a Complex Neonatal Physiological Model for Testing of Novel Closed-Loop Inspired Oxygen Fraction Controllers

Ráfl, Jakub; Bachman, Thomas E.; Martínek, Tomáš; Tejkl, Leos; Huttova, Veronika; Kudrna, Petr; Roubík, Karel

doi:10.1007/978-981-10-9035-6_134

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…Therefore, motion artifact and desaturation may have a similar SpO 2 recording, and motion artifact could be interpreted as the true desaturation and vice versa, or motion artifact can obscure the true desaturation with noise [23]. Abrupt changes in the SpO 2 signal due to desaturations are reflected in the pulse oximeter module, as they are generated by the overall model of neonatal oxygenation [8]. However, motion artifacts leading to significant drops in the SpO 2 signal (drops greater than 10% within 30 s) are not included in our model and should be modeled separately in a future study with simultaneous recording of SpO 2 signal and motion capture.…”

Section: Discussionmentioning

confidence: 99%

“…In the last ten years, many articles have introduced closed-loop control algorithms of oxygenation in neonates, but a complex clinical study to compare the effectiveness of those various algorithms is still missing [6]. Clinical tests of the new oxygenation control algorithms of neonates bring safety and ethical risks, but a mathematical model of oxygenation in neonates can allow for the in silico simulation of oxygenation and preliminary comparison of the control algorithms [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Other studies [27,28] assessed the quality of SaO 2 estimation from photoplethysmography and detected poor-quality segments by methods of machine learning. However, the considered global model of neonatal oxygenation [8] does not consider the pulsatility in the cardiovascular system and, thus, the photoplethysmography curve. Instead, the pulse oximeter module is treated as a black box that converts a continuous SaO 2 signal to a stream of trend SpO 2 values, reported every 2 s, that is used by automatic closed-loop control algorithms to adjust the fraction of inspired oxygen in the model input.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Description of SaO2–SpO2 Relationship for Model of Oxygenation in Premature Infants

et al. 2022

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A pulse oximeter model linking arterial (SaO2) and peripheral (SpO2) oxygen saturation is the terminal part of a mathematical model of neonatal oxygen transport. Previous studies have confirmed the overestimation of oxygen saturation measured by pulse oximetry in neonates compared to arterial oxygen saturation and the large variability of measured values over time caused by measurement inaccuracies. This work aimed to determine the SpO2 measurement noise that affects the biased SpO2 value at each time point and integrate the noise description with the systematic bias between SaO2 and SpO2. The SaO2–SpO2 bias was based on previously published clinical data from pathological patients younger than 60 days requiring ventilatory support. The statistical properties of the random SpO2 measurement noise were estimated from the SpO2 continuous recordings of 21 pathological and 21 physiological neonates. The result of the work is a comprehensive characterization of the properties of a pulse oximeter model describing the transfer of the input SaO2 value to the output SpO2 value, including the bias and noise typical for the bedside monitoring of neonates. These results will help to improve a computer model of neonatal oxygen transport.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical Description of SaO2–SpO2 Relationship for Model of Oxygenation in Premature Infants

et al. 2022

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“…With the rapid development of medical imaging technology, image processing has become an indispensable part of modern medicine [1][2][3][4]. Particularly in the field of critical care, the swift and accurate assessment of a patient's physiological status is vital for clinical decision-making [5,6]. The integration of big data and artificial intelligence technologies has increasingly made image-based automatic detection and condition assessment a focus of research.…”

Section: Introductionmentioning

confidence: 99%

Automated Physiological Status Detection and Disease Evaluation of Critically Ill Patients via Image Processing Technologies

Wang,

Ma,

Zhao

et al. 2024

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In the realm of critical care, precise detection of physiological status and disease evaluation are paramount for effective treatment and nursing. With the continuous advancement of medical imaging technology, image processing techniques herald new possibilities for achieving these objectives. This study is dedicated to enhancing the automation level and accuracy of physiological status monitoring and disease evaluation for critical patients through cutting-edge image analysis technologies. The background section explores the current application of medical imaging in critical care, underscoring the significance and developmental trends of automated image processing in this domain. The state-of-the-art review highlights existing image segmentation and classification methods, addressing challenges encountered in complex critical care scenarios, such as insufficient segmentation precision and weak feature representation capabilities. To tackle these issues, a novel image segmentation approach based on boundary learning and enhancement (BLE), along with a disease severity classification model leveraging feature augmentation, is proposed. Through optimization of deep learning models, the segmentation part strengthens the identification of subtle boundaries in images depicting the physiological status of critical patients, thereby enhancing segmentation accuracy and robustness. In the aspect of disease classification, the study improves the model's ability to recognize features indicative of the patients' condition through feature enhancement techniques, leading to heightened classification precision. The application of these methodologies not only elevates the quality of care but also aids healthcare professionals in making more rapid and accurate decisions. The outcomes of this study hold significant implications for advancing the level of automation in physiological status monitoring and disease evaluation of critical patients, and they positively impact the further development of medical imaging technology in clinical applications.

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Design and Demonstration of a Complex Neonatal Physiological Model for Testing of Novel Closed-Loop Inspired Oxygen Fraction Controllers

Cited by 2 publications

References 17 publications

Statistical Description of SaO2–SpO2 Relationship for Model of Oxygenation in Premature Infants

Statistical Description of SaO2–SpO2 Relationship for Model of Oxygenation in Premature Infants

Automated Physiological Status Detection and Disease Evaluation of Critically Ill Patients via Image Processing Technologies

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