Guidance for reporting artificial intelligence technology evaluations for ultrasound scanning in regional anaesthesia (GRAITE-USRA): an international multidisciplinary consensus reporting framework.
Authors
Affiliations (12)
Affiliations (12)
- Department of Anaesthesia, Chelsea and Westminster Hospital, London, UK.
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
- Unit of Injury, Recovery and Inflammation Science, School of Medicine, University of Nottingham, Nottingham, UK.
- Department of Anaesthesia and Critical Care, Nottingham University Hospitals NHS Trust, Nottingham, UK.
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
- Department of Anaesthesia and Critical Care, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
- Centre for Applied Health and Social Care Research, Sheffield Hallam University, Sheffield, UK.
- Newham University Hospital, London, UK.
- King's College Hospitals NHS Foundation Trust, London, UK.
- NHS Ayrshire and Arran, Scotland.
- Department of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.
- Department of Targeted Intervention, University College London, London, UK.
Abstract
The application of artificial intelligence to enhance the clinical practice of ultrasound-guided regional anaesthesia is of increasing interest to clinicians, researchers and industry. The lack of standardised reporting for studies in this field hinders the comparability, reproducibility and integration of findings. We aimed to develop a consensus-based reporting guideline for research evaluating artificial intelligence applications for ultrasound scanning in regional anaesthesia. We followed methodology recommended by the EQUATOR Network for the development of reporting guidelines. Review of published literature and expert consultation generated a preliminary list of candidate reporting items. An international, multidisciplinary, modified Delphi process was then undertaken, involving experts from clinical practice, academia and industry. Two rounds of expert consultation were conducted, in which participants evaluated each item for inclusion in a final reporting guideline, followed by an online discussion. A total of 67 experts participated in the first Delphi round, 63 in the second round and 25 in the roundtable consensus meeting. The GRAITE-USRA reporting guideline comprises 40 items addressing key aspects of reporting in artificial intelligence research for ultrasound scanning in regional anaesthesia. Specific items include ultrasound acquisition protocols and operator expertise, which are not covered in existing artificial intelligence reporting guidelines. The GRAITE-USRA reporting guideline provides a minimum set of recommendations for artificial intelligence-related research for ultrasound scanning in regional anaesthesia. Its adoption will promote consistent reporting standards, enhance transparency, improve study reproducibility and ultimately support the effective integration of evidence into clinical practice.