Dataset Information

Oxygenation performance assessment of an artificial lung in different central anatomic configurations.

ABSTRACT:

Objectives

Aim of this work was to characterize possible central anatomical configurations in which a future artificial lung (AL) could be connected, in terms of oxygenation performance.

Methods

Pulmonary and systemic circulations were simulated using a numerical and an in vitro approach. The in vitro simulation was carried out in a mock loop in three phases: (1) normal lung, (2) pulmonary shunt (50% and 100%), and (3) oxygenator support in three anatomical configurations: right atrium-pulmonary artery (RA-PA), pulmonary artery-left atrium (PA-LA), and aorta-left atrium (Ao-LA). The numerical simulation was performed for the oxygenator support phase. The oxygen saturation (SO₂) of the arterial blood was plotted over time for two percentages of pulmonary shunt and three blood flow rates through the oxygenator.

Results

During the pulmonary shunt phase, SO₂ reached a steady state value (of 68% for a 50% shunt and of nearly 0% for a 100% shunt) 20 min after the shunt was set. During the oxygenator support phase, physiological values of SO₂ were reached for RA-PA and PA-LA, in case of a 50% pulmonary shunt. For the same conditions, Ao-LA could reach a maximum SO₂ of nearly 60%. Numerical results were congruous to the in vitro simulation ones.

Conclusions

Both in vitro and numerical simulations were able to properly characterize oxygenation properties of a future AL depending on its placement. Different anatomical configurations perform differently in terms of oxygenation. Right to right and right to left connections perform better than left to left ones.

SUBMITTER: Hima F

PROVIDER: S-EPMC10160396 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

Oxygenation performance assessment of an artificial lung in different central anatomic configurations.

Hima Flutura F Kalverkamp Sebastian S Kashefi Ali A Mottaghy Khosrow K Zayat Rachad R Strudthoff Lasse L Spillner Jan J Mouzakis Foivos F

The International journal of artificial organs 20230412 5

<h4>Objectives</h4>Aim of this work was to characterize possible central anatomical configurations in which a future artificial lung (AL) could be connected, in terms of oxygenation performance.<h4>Methods</h4>Pulmonary and systemic circulations were simulated using a numerical and an in vitro approach. The in vitro simulation was carried out in a mock loop in three phases: (1) normal lung, (2) pulmonary shunt (50% and 100%), and (3) oxygenator support in three anatomical configurations: right a ...[more]

PMID: 37051677

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Project description:BackgroundThe coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm).MethodsA total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined.ResultsA total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively.ConclusionsThe AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.

Dataset Information

Oxygenation performance assessment of an artificial lung in different central anatomic configurations.

Objectives

Methods

Results

Conclusions

Publications

Oxygenation performance assessment of an artificial lung in different central anatomic configurations.

Similar Datasets

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