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Background: In artificial intelligence for ultrasound-guided regional anaesthesia, accurate nerve identification is essential. The technology community typically favours objective metrics of pixel overlap on still-frame images, whereas clinical assessments often use subjective evaluation of cine loops by physician experts. No clinically acceptable threshold of pixel overlap has been defined for nerve segmentation. We investigated the relationship between these approaches and identify thresholds for objective pixel-based metrics when clinical evaluations identify high-quality nerve segmentation., Methods: cNerve™ is a deep learning segmentation tool on GE Healthcare's Venue™ ultrasound systems. It highlights nerves of the interscalene-supraclavicular-level brachial plexus, femoral, and popliteal-level sciatic block regions. Expert anaesthesiologists subjectively rated overall segmentation quality of cNerve™ on ultrasound cine loop sequences using a 1-5 Likert scale (1 = poor; 5 = excellent). Objective assessments of nerve segmentation, using the Intersection over Union and Dice similarity coefficient metrics, were applied to frames from sequences rated 5., Results: A total of 173 still image frames were analysed. The median Intersection over Union for nerves was 0.49, and the median Dice similarity coefficient was 0.65, indicating variable performance based on objective metrics, despite subjective clinical evaluations rating the artificial intelligence-generated nerve segmentation as excellent., Conclusions: Variable objective segmentation metric scores correspond to excellent performance on clinically oriented assessment and lack the context provided by subjective expert evaluations. Further work is needed to establish standardised evaluation criteria that incorporate both objective pixel-based and subjective clinical assessments. Collaboration between clinicians and technologists is needed to develop these evaluation methods for improved clinical applicability., Competing Interests: Declarations of interest BVD has received fees from GE Healthcare and is a consultant for GE Healthcare. OM, TG, and LM have received fees from GE Healthcare. JSB is an employee of GE Healthcare and was previously a Senior Clinical Advisor for Intelligent Ultrasound. He has acted as a consultant for AutonomUS and received speaker fees from the Belgian Association for Regional Anaesthesia., (Copyright © 2025 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Regional anaesthesia use is growing worldwide, and there is an increasing emphasis on research in regional anaesthesia to improve patient outcomes. However, priorities for future study remain unclear. We therefore conducted an international research prioritisation exercise, setting the agenda for future investigators and funding bodies., Methods: We invited members of specialist regional anaesthesia societies from six continents to propose research questions that they felt were unanswered. These were consolidated into representative indicative questions, and a literature review was undertaken to determine if any indicative questions were already answered by published work. Unanswered indicative questions entered a three-round modified Delphi process, whereby 29 experts in regional anaesthesia (representing all participating specialist societies) rated each indicative question for inclusion on a final high priority shortlist. If ≥75% of participants rated an indicative question as 'definitely' include in any round, it was accepted. Indicative questions rated as 'definitely' or 'probably' by <50% of participants in any round were excluded. Retained indicative questions were further ranked based on the rating score in the final Delphi round. The final research priorities were ratified by the Delphi expert group., Results: There were 1318 responses from 516 people in the initial survey, from which 71 indicative questions were formed, of which 68 entered the modified Delphi process. Eleven 'highest priority' research questions were short listed, covering themes of pain management; training and assessment; clinical practice and efficacy; technology and equipment., Conclusions: We prioritised unanswered research questions in regional anaesthesia. These will inform a coordinated global research strategy for regional anaesthesia and direct investigators to address high-priority areas., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: ScanNav TM Anatomy Peripheral Nerve Block (ScanNav™) is an artificial intelligence (AI)-based device that produces a colour overlay on real-time B-mode ultrasound to highlight key anatomical structures for regional anaesthesia. This study compares consistency of identification of sono-anatomical structures between expert ultrasonographers and ScanNav™., Methods: Nineteen experts in ultrasound-guided regional anaesthesia (UGRA) annotated 100 structures in 30 ultrasound videos across six anatomical regions. These annotations were compared with each other to produce a quantitative assessment of the level of agreement amongst human experts. The AI colour overlay was then compared with all expert annotations. Differences in human-human and human-AI agreement are presented for each structure class (artery, muscle, nerve, fascia/serosal plane) and structure. Clinical context is provided through subjective assessment data from UGRA experts., Results: For human-human and human-AI annotations, agreement was highest for arteries (mean Dice score 0.88/0.86), then muscles (0.80/0.77), and lowest for nerves (0.48/0.41). Wide discrepancy exists in consistency for different structures, both with human-human and human-AI comparisons; highest for sartorius muscle (0.91/0.92) and lowest for the radial nerve (0.21/0.27)., Conclusions: Human experts and the AI system both showed the same pattern of agreement in sono-anatomical structure identification. The clinical significance of the differences presented must be explored; however the perception that human expert opinion is uniform must be challenged. Elements of this assessment framework could be used for other devices to allow consistent evaluations that inform clinical training and practice. Anaesthetists should be actively engaged in the development and adoption of new AI technology., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: 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., Methods: 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., Results: 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., Conclusions: 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., Clinical Trial Registration: NCT05156099., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Ultrasonound is used to identify anatomical structures during regional anaesthesia and to guide needle insertion and injection of local anaesthetic. ScanNav Anatomy Peripheral Nerve Block (Intelligent Ultrasound, Cardiff, UK) is an artificial intelligence-based device that produces a colour overlay on real-time B-mode ultrasound to highlight anatomical structures of interest. We evaluated the accuracy of the artificial-intelligence colour overlay and its perceived influence on risk of adverse events or block failure., Methods: Ultrasound-guided regional anaesthesia experts acquired 720 videos from 40 volunteers (across nine anatomical regions) without using the device. The artificial-intelligence colour overlay was subsequently applied. Three more experts independently reviewed each video (with the original unmodified video) to assess accuracy of the colour overlay in relation to key anatomical structures (true positive/negative and false positive/negative) and the potential for highlighting to modify perceived risk of adverse events (needle trauma to nerves, arteries, pleura, and peritoneum) or block failure., Results: The artificial-intelligence models identified the structure of interest in 93.5% of cases (1519/1624), with a false-negative rate of 3.0% (48/1624) and a false-positive rate of 3.5% (57/1624). Highlighting was judged to reduce the risk of unwanted needle trauma to nerves, arteries, pleura, and peritoneum in 62.9-86.4% of cases (302/480 to 345/400), and to increase the risk in 0.0-1.7% (0/160 to 8/480). Risk of block failure was reported to be reduced in 81.3% of scans (585/720) and to be increased in 1.8% (13/720)., Conclusions: 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., Clinical Trial Registration: NCT04906018., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Many intricate pathways contribute to the timely control of gene expression during development. Polycomb repressive complexes (PRC1 and PRC2) and long non-coding RNAs (lncRNAs) are players associated with gene repression in various developmental processes such as X chromosome inactivation (XCI) and genomic imprinting. Historically, lncRNAs were proposed to directly recruit PRC2. However, recent evidence suggests that promiscuous interactions between PRC2 and RNA fine-tune the function of the complex through a multiplicity of mechanisms. A PRC2-recruitment model was definitively overturned in the paradigm of XCI by Xist RNA, being replaced by a novel mechanism which puts PRC1 in the spotlight. This review focuses on these recent advances in understanding the interplay between RNA and Polycomb complexes for gene expression control., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)