Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia
Ultrasound-guided regional anaesthesia relies on the visualisation of key landmark, target, and safety structures on ultrasound. However, this can be challenging, particularly for inexperienced practitioners. Artificial intelligence (AI) is increasingly being applied to medical image interpretation,...
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| Veröffentlicht in: | British journal of anaesthesia : BJA Jg. 130; H. 2; S. 226 |
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| Sprache: | Englisch |
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01.02.2023
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| ISSN: | 1471-6771, 1471-6771 |
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| Abstract | Ultrasound-guided regional anaesthesia relies on the visualisation of key landmark, target, and safety structures on ultrasound. However, this can be challenging, particularly for inexperienced practitioners. Artificial intelligence (AI) is increasingly being applied to medical image interpretation, including ultrasound. In this exploratory study, we evaluated ultrasound scanning performance by non-experts in ultrasound-guided regional anaesthesia, with and without the use of an assistive AI device.
Twenty-one anaesthetists, all non-experts in ultrasound-guided regional anaesthesia, underwent a standardised teaching session in ultrasound scanning for six peripheral nerve blocks. All then performed a scan for each block; half of the scans were performed with AI assistance and half without. Experts assessed acquisition of the correct block view and correct identification of sono-anatomical structures on each view. Participants reported scan confidence, experts provided a global rating score of scan performance, and scans were timed.
Experts assessed 126 ultrasound scans. Participants acquired the correct block view in 56/62 (90.3%) scans with the device compared with 47/62 (75.1%) without (P=0.031, two data points lost). Correct identification of sono-anatomical structures on the view was 188/212 (88.8%) with the device compared with 161/208 (77.4%) without (P=0.002). There was no significant overall difference in participant confidence, expert global performance score, or scan time.
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.
NCT05156099. |
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| AbstractList | Ultrasound-guided regional anaesthesia relies on the visualisation of key landmark, target, and safety structures on ultrasound. However, this can be challenging, particularly for inexperienced practitioners. Artificial intelligence (AI) is increasingly being applied to medical image interpretation, including ultrasound. In this exploratory study, we evaluated ultrasound scanning performance by non-experts in ultrasound-guided regional anaesthesia, with and without the use of an assistive AI device.
Twenty-one anaesthetists, all non-experts in ultrasound-guided regional anaesthesia, underwent a standardised teaching session in ultrasound scanning for six peripheral nerve blocks. All then performed a scan for each block; half of the scans were performed with AI assistance and half without. Experts assessed acquisition of the correct block view and correct identification of sono-anatomical structures on each view. Participants reported scan confidence, experts provided a global rating score of scan performance, and scans were timed.
Experts assessed 126 ultrasound scans. Participants acquired the correct block view in 56/62 (90.3%) scans with the device compared with 47/62 (75.1%) without (P=0.031, two data points lost). Correct identification of sono-anatomical structures on the view was 188/212 (88.8%) with the device compared with 161/208 (77.4%) without (P=0.002). There was no significant overall difference in participant confidence, expert global performance score, or scan time.
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.
NCT05156099. Ultrasound-guided regional anaesthesia relies on the visualisation of key landmark, target, and safety structures on ultrasound. However, this can be challenging, particularly for inexperienced practitioners. Artificial intelligence (AI) is increasingly being applied to medical image interpretation, including ultrasound. In this exploratory study, we evaluated ultrasound scanning performance by non-experts in ultrasound-guided regional anaesthesia, with and without the use of an assistive AI device.BACKGROUNDUltrasound-guided regional anaesthesia relies on the visualisation of key landmark, target, and safety structures on ultrasound. However, this can be challenging, particularly for inexperienced practitioners. Artificial intelligence (AI) is increasingly being applied to medical image interpretation, including ultrasound. In this exploratory study, we evaluated ultrasound scanning performance by non-experts in ultrasound-guided regional anaesthesia, with and without the use of an assistive AI device.Twenty-one anaesthetists, all non-experts in ultrasound-guided regional anaesthesia, underwent a standardised teaching session in ultrasound scanning for six peripheral nerve blocks. All then performed a scan for each block; half of the scans were performed with AI assistance and half without. Experts assessed acquisition of the correct block view and correct identification of sono-anatomical structures on each view. Participants reported scan confidence, experts provided a global rating score of scan performance, and scans were timed.METHODSTwenty-one anaesthetists, all non-experts in ultrasound-guided regional anaesthesia, underwent a standardised teaching session in ultrasound scanning for six peripheral nerve blocks. All then performed a scan for each block; half of the scans were performed with AI assistance and half without. Experts assessed acquisition of the correct block view and correct identification of sono-anatomical structures on each view. Participants reported scan confidence, experts provided a global rating score of scan performance, and scans were timed.Experts assessed 126 ultrasound scans. Participants acquired the correct block view in 56/62 (90.3%) scans with the device compared with 47/62 (75.1%) without (P=0.031, two data points lost). Correct identification of sono-anatomical structures on the view was 188/212 (88.8%) with the device compared with 161/208 (77.4%) without (P=0.002). There was no significant overall difference in participant confidence, expert global performance score, or scan time.RESULTSExperts assessed 126 ultrasound scans. Participants acquired the correct block view in 56/62 (90.3%) scans with the device compared with 47/62 (75.1%) without (P=0.031, two data points lost). Correct identification of sono-anatomical structures on the view was 188/212 (88.8%) with the device compared with 161/208 (77.4%) without (P=0.002). There was no significant overall difference in participant confidence, expert global performance score, or scan time.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.CONCLUSIONSUse 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.NCT05156099.CLINICAL TRIAL REGISTRATIONNCT05156099. |
| Author | Sleep, Nick West, Simeon Phillips, David Higham, Helen Macfarlane, Alan J R Noble, J Alison Rees, Tom Morecroft, Megan Burckett-St Laurent, David Margetts, Steve Vasalauskaite, Asta Bowness, James S Harris, Catherine |
| Author_xml | – sequence: 1 givenname: James S surname: Bowness fullname: Bowness, James S email: james.bowness@jesus.ox.ac.uk organization: Oxford Simulation, Teaching and Research Centre, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK. Electronic address: james.bowness@jesus.ox.ac.uk – sequence: 2 givenname: Alan J R surname: Macfarlane fullname: Macfarlane, Alan J R organization: Department of Anaesthesia, Glasgow Royal Infirmary, Glasgow, UK; School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, UK – sequence: 3 givenname: David surname: Burckett-St Laurent fullname: Burckett-St Laurent, David organization: Department of Anaesthesia, Royal Cornwall Hospitals NHS Trust, Truro, UK – sequence: 4 givenname: Catherine surname: Harris fullname: Harris, Catherine organization: Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK – sequence: 5 givenname: Steve surname: Margetts fullname: Margetts, Steve organization: Intelligent Ultrasound, Cardiff, UK – sequence: 6 givenname: Megan surname: Morecroft fullname: Morecroft, Megan organization: Intelligent Ultrasound, Cardiff, UK – sequence: 7 givenname: David surname: Phillips fullname: Phillips, David organization: Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK – sequence: 8 givenname: Tom surname: Rees fullname: Rees, Tom organization: Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK – sequence: 9 givenname: Nick surname: Sleep fullname: Sleep, Nick organization: Intelligent Ultrasound, Cardiff, UK – sequence: 10 givenname: Asta surname: Vasalauskaite fullname: Vasalauskaite, Asta organization: Intelligent Ultrasound, Cardiff, UK – sequence: 11 givenname: Simeon surname: West fullname: West, Simeon organization: Department of Anaesthesia, University College London, London, UK – sequence: 12 givenname: J Alison surname: Noble fullname: Noble, J Alison organization: Institute of Biomedical Engineering, University of Oxford, UK – sequence: 13 givenname: Helen surname: Higham fullname: Higham, Helen organization: Oxford Simulation, Teaching and Research Centre, University of Oxford, Oxford, UK; Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK |
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| Keywords | ultrasonography regional anaesthesia artificial intelligence sono-anatomy ultrasound peripheral nerve block |
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| SubjectTerms | Anesthesia, Conduction - methods Artificial Intelligence Humans Nerve Block - methods Ultrasonography Ultrasonography, Interventional - methods |
| Title | Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia |
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