Assistive artificial intelligence for enhanced patient access to ultrasound-guided regional anaesthesia

Regional anaesthesia research priorities: a Regional Anaesthesia UK (RA-UK) priority setting partnership involving patients, carers and healthcare professionals

INTRODUCTION

Regional anaesthesia provides important clinical benefits to patients but is underutilised. A barrier to widespread adoption may be the focus of regional anaesthesia research on novel techniques rather than evaluating and optimising existing approaches. Research priorities in regional anaesthesia identified by anaesthetists have been published, but the views of patients, carers and other healthcare professionals have not been considered previously. Therefore, we launched a multidisciplinary research priority setting partnership that aimed to establish key regional anaesthesia research priorities for the UK.

METHODS

Research suggestions from key stakeholders (defined by their interaction with regional anaesthesia) were gathered using an online survey. These suggestions were analysed to identify common themes and then combined to formulate indicative research questions. After an extensive literature review, unanswered and partially answered questions were prioritised via an interim online survey and then ranked as a top 10 list during a final live virtual multidisciplinary prioritisation workshop.

RESULTS

In total, 210 individuals completed the initial survey and suggested 518 research questions. Fifty-seven indicative questions were formed, of which three were considered fully answered after literature review and one not feasible. The interim online survey received 335 responses, which identified the 24 highest priority questions from the 53 presented. At the final live prioritisation workshop, through a nominal group process, we identified the top 10 regional anaesthesia research priorities. These aligned with three broad thematic areas: pain management (two questions); patient safety (six questions); and recovery from surgery (two questions).

DISCUSSION

This initiative has resulted in a list of research questions prioritised by patients, carers and a multidisciplinary group of healthcare professionals that should be used to inform and support future regional anaesthesia research in the UK.

Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example

A recent study by Suissa and colleagues explored the clinical relevance of a medical image segmentation metric (Dice metric) commonly used in the field of artificial intelligence (AI). They showed that pixel-wise agreement for physician identification of structures on ultrasound images is variable, and a relatively low Dice metric (0.34) correlated to a substantial agreement on subjective clinical assessment. We highlight the need to bring structure and clinical perspective to the evaluation of medical AI, which clinicians are best placed to direct.

Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines

BACKGROUND

Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia.

METHODS

A literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed.

RESULTS

In total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016-17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation.

CONCLUSIONS

There is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption.