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AI-Enabled, Ultrasound-Guided Handheld Robotic Device for Femoral Vascular Access.
- Source :
-
Biosensors [Biosensors (Basel)] 2021 Dec 18; Vol. 11 (12). Date of Electronic Publication: 2021 Dec 18. - Publication Year :
- 2021
-
Abstract
- Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well as invasive sensing and emerging life-saving interventions. However, central access is normally performed by highly experienced critical care physicians in a hospital setting. We developed a handheld AI-enabled interventional device, AI-GUIDE (Artificial Intelligence Guided Ultrasound Interventional Device), capable of directing users with no ultrasound or interventional expertise to catheterize a deep blood vessel, with an initial focus on the femoral vein. AI-GUIDE integrates with widely available commercial portable ultrasound systems and guides a user in ultrasound probe localization, venous puncture-point localization, and needle insertion. The system performs vascular puncture robotically and incorporates a preloaded guidewire to facilitate the Seldinger technique of catheter insertion. Results from tissue-mimicking phantom and porcine studies under normotensive and hypotensive conditions provide evidence of the technique's robustness, with key performance metrics in a live porcine model including: a mean time to acquire femoral vein insertion point of 53 ± 36 s (5 users with varying experience, in 20 trials), a total time to insert catheter of 80 ± 30 s (1 user, in 6 trials), and a mean number of 1.1 (normotensive, 39 trials) and 1.3 (hypotensive, 55 trials) needle insertion attempts (1 user). These performance metrics in a porcine model are consistent with those for experienced medical providers performing central vascular access on humans in a hospital.
Details
- Language :
- English
- ISSN :
- 2079-6374
- Volume :
- 11
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- Biosensors
- Publication Type :
- Academic Journal
- Accession number :
- 34940279
- Full Text :
- https://doi.org/10.3390/bios11120522