User validation of an AI-assisted ultrasound guidance system for rapid vascular access

Abstract Objectives Vascular cannulation enables life-saving resuscitation in traumatic hemorrhage. The AI-GUIDE-LL is a handheld ultrasound-guided robotic device enabling inexperienced medical providers to obtain femoral vascular access. Device efficacy has been demonstrated in expert-operated porcine models, though performance among inexperienced operators and in humans is unknown. We evaluated the AI-GUIDE-LL’s performance among novice clinicians to target human femoral vessels and accurately deploy needles in phantoms. Materials and methods This single-site prospective study recruited medical professionals without ultrasound-guided procedural experience. Users attempted femoral artery needle insertion in five human CT-derived inguinal phantoms using fluid aspiration as the reference standard, with three needle insertions permitted per phantom. Subsequently, they used the device to target healthy human volunteer femoral vessels, assessing target vessel localization and common femoral segment identification. Success was defined by expert review of ultrasound images and video recordings. Results Thirty clinicians (seven advanced practice providers, five EMTs, nine physicians, and nine registered nurses) achieved correct needle insertion in phantoms in 93% (140/150) of trials, with a median completion time of 44 s and a mean of 1.1 attempts. 28 volunteers were scanned by 28 clinicians. Clinicians successfully targeted 97% (93/96) of vessels with a median time of 25 s. Common femoral segment confirmation was achieved in only 20% of trials. Conclusion The AI-GUIDE-LL enables rapid, accurate femoral vessel localization and needle insertion among novice clinicians, supporting feasibility for field-forward military and point-of-care civilian settings. Reliable segment-specific localization above the femoral bifurcation, necessary for large-bore arterial procedures, remains an important limitation requiring further work. Key Points Question AI-GUIDE-LL is a handheld robotic system that integrates artificial intelligence and ultrasound guidance to assist non-expert users in performing femoral vascular access for rapid point-of-care interventions . Findings Clinicians with limited ultrasound experience successfully localized femoral vessels, achieving 93% phantom needle insertion success and 97% target vessel localization in human trials . Clinical relevance AI-GUIDE-LL may enable clinicians with limited ultrasound training to perform timely femoral cannulation for life-saving interventions, including fluid resuscitation in traumatic hemorrhage, particularly in military or resource-limited environments .

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Publication Details

Journal
European Radiology
Published
2026-09-11
DOI
https://doi.org/10.1007/s00330-026-12854-4
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

User validation of an AI-assisted ultrasound guidance system for rapid vascular access

Lars Gjesteby, Joshua S. Werblin, Samuel B. Kesner, Mary Waugh et al.
European Radiology
Soft Robotics and Applications
article

User validation of an AI-assisted ultrasound guidance system for rapid vascular access

Lars Gjesteby, Joshua S. Werblin, Samuel B. Kesner, Mary Waugh, Brian A. Telfer, Alec Carruthers, Benjamin W. Roop, Mateusz Wolak, Anthony E. Samir, Theodore T. Pierce, Nancy D. DeLosa, Samay Prakash, Anusha Priya, Mark Ottensmeyer, Eugene Cheah
article en

Abstract

Abstract Objectives Vascular cannulation enables life-saving resuscitation in traumatic hemorrhage. The AI-GUIDE-LL is a handheld ultrasound-guided robotic device enabling inexperienced medical providers to obtain femoral vascular access. Device efficacy has been demonstrated in expert-operated porcine models, though performance among inexperienced operators and in humans is unknown. We evaluated the AI-GUIDE-LL’s performance among novice clinicians to target human femoral vessels and accurately deploy needles in phantoms. Materials and methods This single-site prospective study recruited medical professionals without ultrasound-guided procedural experience. Users attempted femoral artery needle insertion in five human CT-derived inguinal phantoms using fluid aspiration as the reference standard, with three needle insertions permitted per phantom. Subsequently, they used the device to target healthy human volunteer femoral vessels, assessing target vessel localization and common femoral segment identification. Success was defined by expert review of ultrasound images and video recordings. Results Thirty clinicians (seven advanced practice providers, five EMTs, nine physicians, and nine registered nurses) achieved correct needle insertion in phantoms in 93% (140/150) of trials, with a median completion time of 44 s and a mean of 1.1 attempts. 28 volunteers were scanned by 28 clinicians. Clinicians successfully targeted 97% (93/96) of vessels with a median time of 25 s. Common femoral segment confirmation was achieved in only 20% of trials. Conclusion The AI-GUIDE-LL enables rapid, accurate femoral vessel localization and needle insertion among novice clinicians, supporting feasibility for field-forward military and point-of-care civilian settings. Reliable segment-specific localization above the femoral bifurcation, necessary for large-bore arterial procedures, remains an important limitation requiring further work. Key Points Question AI-GUIDE-LL is a handheld robotic system that integrates artificial intelligence and ultrasound guidance to assist non-expert users in performing femoral vascular access for rapid point-of-care interventions . Findings Clinicians with limited ultrasound experience successfully localized femoral vessels, achieving 93% phantom needle insertion success and 97% target vessel localization in human trials . Clinical relevance AI-GUIDE-LL may enable clinicians with limited ultrasound training to perform timely femoral cannulation for life-saving interventions, including fluid resuscitation in traumatic hemorrhage, particularly in military or resource-limited environments .

European Radiology
Harvard University (US), Massachusetts General Hospital (US), MIT Lincoln Laboratory (US), Massachusetts Institute of Technology (US)
U.S. Department of Defense
Openalex Percentile: Top 20%
Soft Robotics and Applications
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