AI Clinical Studies
Clinical studies
Our products have featured in a variety of clinical studies to assess the value of simulation and artificial intelligence in advancing ultrasound.
International consensus on anatomical structures to identify on ultrasound for the performance of basic blocks in ultrasound-guided regional anesthesia
James Simeon Bowness, Amit Pawa, Lloyd Turbitt, Boyne Bellew, Nigel Bedforth, David Burckett-St Laurent, Alain Delbos, Nabil Elkassabany, Jenny Ferry, Ben Fox, James L H French, Calum Grant, Ashwani Gupta, William Harrop-Griffiths, Nat Haslam, Helen Higham, Rosemary Hogg, David F Johnston, Rachel Joyce Kearns, Sandra Kopp, Clara Lobo, Sonya McKinlay, Stavros Memtsoudis, Peter Merjavy, Eleni Moka, Madan Narayanan, Samer Narouze, J Alison Noble, David Phillips, Meg Rosenblatt, Amy Sadler, Maria Paz Sebastian, Alasdair Taylor, Athmaja Thottungal, Luis Fernando Valdés-Vilches, Thomas Volk, Simeon West, Morné Wolmarans, Jonathan Womack and James Robert Macfarlane | This study aimed to produce standardized recommendations for core (minimum) structures to identify during seven basic blocks.
Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia
James Simeon Bowness, Kariem El- Boghdadly, Glenn Woodworth, Alison Noble, Helen Higham, David Burckett- St Laurent | This study explores the utility of a novel artificial intelligence (AI) device designed to assist in this task (ScanNav Anatomy Peripheral Nerve Block; ScanNav), which applies a color overlay on real- time ultrasound to highlight key anatomical structures.
Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia
James S. Bowness, Alan J.R. Macfarlane, David Burckett-St Laurent, Catherine Harris, Steve Margetts, Megan Morecroft, David Phillips, Tom Rees, Nick Sleep, Asta Vasalauskaite, Simeon West, J. Alison Noble, Helen Higham | Use of an assistive AI device was associated with improved ultrasound image acquisition and interpretation. Such technology holds potential to augment performance of ultrasound scanning for regional anaesthesia by non-experts, potentially expanding patient access to these techniques.
Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study
James S. Bowness, David Burckett-St Laurent, Nadia Hernandez, Pearse A. Keane, Clara Lobo, Steve Margetts, Eleni Moka, Amit Pawa, Meg Rosenblatt, Nick Sleep, Alasdair Taylor, Glenn Woodworth, Asta Vasalauskaite, J. Alison Noble, Helen Higham | Artificial intelligence-based devices can potentially aid image acquisition and interpretation in ultrasound-guided regional anaesthesia. Further studies are necessary to demonstrate their effectiveness in supporting training and clinical practice.
Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anesthesia
James Bowness, Ourania Varsou, Lloyd Turbitt, David Burkett-St Laurent | This investigation evaluates the performance of an assistive artificial intelligence (AI) system in aiding the identification of anatomical structures on ultrasound. Three independent experts in regional anesthesia reviewed 40 ultrasound scans of seven body regions.
Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia
J. Bowness, K. El-Boghdadly, D. Burckett-St Laurent ScanNav AnatomyGuide | This study presents the case for the use of artificial intelligence (AI) in identifying key anatomical features to facilitate ultrasound‐guided regional anaesthesia.
