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Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic

Publication ,  Conference
Bishawi, M; Kaplan, M; Chidyagwai, S; Cappiello, J; Cherry, A; MacLeod, D; Gall, K; Evans, N; Kim, M; Shaha, R; Whittle, J; Hollidge, M ...
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2022

In the early days of the COVID-19 pandemic, there was a pressing need for an expansion of the ventilator capacity in response to the COVID19 pandemic. Reserved for dire situations, ventilator splitting is complex, and has previously been limited to patients with similar pulmonary compliances and tidal volume requirements. To address this need, we developed a system to enable rapid and efficacious splitting between two or more patients with varying lung compliances and tidal volume requirements. We present here a computational framework to both drive device design and inform patient-specific device tuning. By creating a patient- and ventilator-specific airflow model, we were able to identify pressure-controlled splitting as preferable to volume-controlled as well create a simulation-guided framework to identify the optimal airflow resistor for a given patient pairing. In this work, we present the computational model, validation of the model against benchtop test lungs and standard-of-care ventilators, and the methods that enabled simulation of over 200 million patient scenarios using 800,000 compute hours in a 72 h period.

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Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2022

Volume

13352 LNCS

Start / End Page

137 / 149

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

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Bishawi, M., Kaplan, M., Chidyagwai, S., Cappiello, J., Cherry, A., MacLeod, D., … Randles, A. (2022). Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13352 LNCS, pp. 137–149). https://doi.org/10.1007/978-3-031-08757-8_13
Bishawi, M., M. Kaplan, S. Chidyagwai, J. Cappiello, A. Cherry, D. MacLeod, K. Gall, et al. “Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13352 LNCS:137–49, 2022. https://doi.org/10.1007/978-3-031-08757-8_13.
Bishawi M, Kaplan M, Chidyagwai S, Cappiello J, Cherry A, MacLeod D, et al. Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 137–49.
Bishawi, M., et al. “Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13352 LNCS, 2022, pp. 137–49. Scopus, doi:10.1007/978-3-031-08757-8_13.
Bishawi M, Kaplan M, Chidyagwai S, Cappiello J, Cherry A, MacLeod D, Gall K, Evans N, Kim M, Shaha R, Whittle J, Hollidge M, Truskey G, Randles A. Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 137–149.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2022

Volume

13352 LNCS

Start / End Page

137 / 149

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences