Research priorities in regional anaesthesia: an international Delphi study.

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.

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.

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.

Standardizing nomenclature in regional anesthesia: an ASRA-ESRA Delphi consensus study of upper and lower limb nerve blocks.

BACKGROUND: Inconsistent nomenclature and anatomical descriptions of regional anesthetic techniques hinder scientific communication and engender confusion; this in turn has implications for research, education and clinical implementation of regional anesthesia. Having produced standardized nomenclature for abdominal wall, paraspinal and chest wall regional anesthetic techniques, we aimed to similarly do so for upper and lower limb peripheral nerve blocks. METHODS: We performed a three-round Delphi international consensus study to generate standardized names and anatomical descriptions of upper and lower limb regional anesthetic techniques. A long list of names and anatomical description of blocks of upper and lower extremities was produced by the members of the steering committee. Subsequently, two rounds of anonymized voting and commenting were followed by a third virtual round table to secure consensus for items that remained outstanding after the first and second rounds. As with previous methodology, strong consensus was defined as ≥75% agreement and weak consensus as 50%-74% agreement. RESULTS: A total of 94, 91 and 65 collaborators participated in the first, second and third rounds, respectively. We achieved strong consensus for 38 names and 33 anatomical descriptions, and weak consensus for five anatomical descriptions. We agreed on a template for naming peripheral nerve blocks based on the name of the nerve and the anatomical location of the blockade and identified several areas for future research. CONCLUSIONS: We achieved consensus on nomenclature and anatomical descriptions of regional anesthetic techniques for upper and lower limb nerve blocks, and recommend using this framework in clinical and academic practice. This should improve research, teaching and learning of regional anesthesia to eventually improve patient care.

Augmented Reality in Ultrasound-Guided Regional Anaesthesia: An Exploratory Study on Models With Potential Implications for Training.

Introduction Needle tip visualisation is a key skill required for the safe practice of ultrasound-guided regional anaesthesia (UGRA). This exploratory study assesses the utility of a novel augmented reality device, NeedleTrainer™, to differentiate between anaesthetists with varying levels of UGRA experience in a simulated environment. Methods Four groups of five participants were recruited (n = 20): novice, early career, experienced anaesthetists, and UGRA experts. Each participant performed three simulated UGRA blocks using NeedleTrainer™ on healthy volunteers (n = 60). The primary aim was to determine whether there was a difference in needle tip visibility, as calculated by the device, between groups of anaesthetists with differing levels of UGRA experience. Secondary aims included the assessment of simulated block conduct by an expert assessor and subjective participant self-assessment. Results The percentage of time the simulated needle tip was maintained in view was higher in the UGRA expert group (57.1%) versus the other three groups (novice 41.8%, early career 44.5%, and experienced anaesthetists 43.6%), but did not reach statistical significance (p = 0.05). An expert assessor was able to differentiate between participants of different UGRA experience when assessing needle tip visibility (novice 3.3 out of 10, early career 5.1, experienced anaesthetists 5.9, UGRA expert group 8.7; p < 0.01) and final needle tip placement (novice 4.2 out of 10, early career 5.6, experienced anaesthetists 6.8, UGRA expert group 8.9; p < 0.01). Subjective self-assessment by participants did not differentiate UGRA experience when assessing needle tip visibility (p = 0.07) or final needle tip placement (p = 0.07). Discussion An expert assessor was able to differentiate between participants with different levels of UGRA experience in this simulated environment. Objective NeedleTrainer™ and subjective participant assessments did not reach statistical significance. The findings are novel as simulated needling using live human subjects has not been assessed before, and no previous studies have attempted to objectively quantify needle tip visibility during simulated UGRA techniques. Future research should include larger sample sizes to further assess the potential use of such technology.

Pilot Study Exploring if an Augmented Reality NeedleTrainer Device Improves Novice Performance of a Simulated Central Venous Catheter Insertion on a Phantom.

Introduction Needle insertion and visualisation skills needed for ultrasound (US)-guided procedures can be challenging to acquire. The novel NeedleTrainer device superimposes a digital holographic needle on a real-time US image display without puncturing a surface. The aim of this randomised control study was to compare the success of trainees performing a simulated central venous catheter insertion on a phantom either with or without prior NeedleTrainer device practice. Methods West of Scotland junior trainees who had not performed insertion of a central venous catheter were randomised into two groups (n=20). Participants undertook standardized online training through a pre-recorded video and training on how to handle a US probe. Group 1 had 10 minutes of supervised training with the NeedleTrainer device. Group 2 were a control group. Participants were assessed on needle insertion to a pre-defined target vein in a phantom. The outcome measures were the time taken for needle placement (secs), number of needle passes (n), operator confidence (0-10), assessor confidence (0-10), and NASA task load index score. Results The mean mental demand score in the control group was 7.65 (SD 3.5) compared to 12.8 (SD 2.2, p=0.005) in the NeedleTrainer group. There was no statistical difference between the groups in any of the other outcome measures. Discussion This was a small pilot study, and small participant numbers may have impacted the statistical significance. There is natural variation of skill within participants that could not have been controlled for. The difference in pressure needed using the NeedleTrainer compared to a real needle may impact the outcome measures.

Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia.

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.

Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study.

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.

Recommendations for anatomical structures to identify on ultrasound for the performance of intermediate and advanced blocks in ultrasound-guided regional anesthesia.

Recent recommendations describe a set of core anatomical structures to identify on ultrasound for the performance of basic blocks in ultrasound-guided regional anesthesia (UGRA). This project aimed to generate consensus recommendations for core structures to identify during the performance of intermediate and advanced blocks. An initial longlist of structures was refined by an international panel of key opinion leaders in UGRA over a three-round Delphi process. All rounds were conducted virtually and anonymously. Blocks were considered twice in each round: for "orientation scanning" (the dynamic process of acquiring the final view) and for "block view" (which visualizes the block site and is maintained for needle insertion/injection). A "strong recommendation" was made if ≥75% of participants rated any structure as "definitely include" in any round. A "weak recommendation" was made if >50% of participants rated it as "definitely include" or "probably include" for all rounds, but the criterion for strong recommendation was never met. Structures which did not meet either criterion were excluded. Forty-one participants were invited and 40 accepted; 38 completed all three rounds. Participants considered the ultrasound scanning for 19 peripheral nerve blocks across all three rounds. Two hundred and seventy-four structures were reviewed for both orientation scanning and block view; a "strong recommendation" was made for 60 structures on orientation scanning and 44 on the block view. A "weak recommendation" was made for 107 and 62 structures, respectively. These recommendations are intended to help standardize teaching and research in UGRA and support widespread and consistent practice.

Artificial Intelligence: Innovation to Assist in the Identification of Sono-anatomy for Ultrasound-Guided Regional Anaesthesia.

Ultrasound-guided regional anaesthesia (UGRA) involves the targeted deposition of local anaesthesia to inhibit the function of peripheral nerves. Ultrasound allows the visualisation of nerves and the surrounding structures, to guide needle insertion to a perineural or fascial plane end point for injection. However, it is challenging to develop the necessary skills to acquire and interpret optimal ultrasound images. Sound anatomical knowledge is required and human image analysis is fallible, limited by heuristic behaviours and fatigue, while its subjectivity leads to varied interpretation even amongst experts. Therefore, to maximise the potential benefit of ultrasound guidance, innovation in sono-anatomical identification is required.Artificial intelligence (AI) is rapidly infiltrating many aspects of everyday life. Advances related to medicine have been slower, in part because of the regulatory approval process needing to thoroughly evaluate the risk-benefit ratio of new devices. One area of AI to show significant promise is computer vision (a branch of AI dealing with how computers interpret the visual world), which is particularly relevant to medical image interpretation. AI includes the subfields of machine learning and deep learning, techniques used to interpret or label images. Deep learning systems may hold potential to support ultrasound image interpretation in UGRA but must be trained and validated on data prior to clinical use.Review of the current UGRA literature compares the success and generalisability of deep learning and non-deep learning approaches to image segmentation and explains how computers are able to track structures such as nerves through image frames. We conclude this review with a case study from industry (ScanNav Anatomy Peripheral Nerve Block; Intelligent Ultrasound Limited). This includes a more detailed discussion of the AI approach involved in this system and reviews current evidence of the system performance.The authors discuss how this technology may be best used to assist anaesthetists and what effects this may have on the future of learning and practice of UGRA. Finally, we discuss possible avenues for AI within UGRA and the associated implications.

Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia.

INTRODUCTION: Ultrasound-guided regional anesthesia (UGRA) involves the acquisition and interpretation of ultrasound images to delineate sonoanatomy. 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. METHODS: Thirty anesthesiologists, 15 non-experts and 15 experts in UGRA, performed 240 ultrasound scans across nine peripheral nerve block regions. Half were performed with ScanNav. After scanning each block region, participants completed a questionnaire on the utility of the device in relation to training, teaching, and clinical practice in ultrasound scanning for UGRA. Ultrasound and color overlay output were recorded from scans performed with ScanNav. Experts present during the scans (real-time experts) were asked to assess potential for increased risk associated with use of the device (eg, needle trauma to safety structures). This was compared with experts who viewed the AI scans remotely. RESULTS: Non-experts were more likely to provide positive and less likely to provide negative feedback than experts (p=0.001). Positive feedback was provided most frequently by non-experts on the potential role for training (37/60, 61.7%); for experts, it was for its utility in teaching (30/60, 50%). Real-time and remote experts reported a potentially increased risk in 12/254 (4.7%) vs 8/254 (3.1%, p=0.362) scans, respectively. DISCUSSION: ScanNav shows potential to support non-experts in training and clinical practice, and experts in teaching UGRA. Such technology may aid the uptake and generalizability of UGRA. TRIAL REGISTRATION NUMBER: NCT04918693.

International consensus on anatomical structures to identify on ultrasound for the performance of basic blocks in ultrasound-guided regional anesthesia.

There is no universally agreed set of anatomical structures that must be identified on ultrasound for the performance of ultrasound-guided regional anesthesia (UGRA) techniques. This study aimed to produce standardized recommendations for core (minimum) structures to identify during seven basic blocks. An international consensus was sought through a modified Delphi process. A long-list of anatomical structures was refined through serial review by key opinion leaders in UGRA. All rounds were conducted remotely and anonymously to facilitate equal contribution of each participant. Blocks were considered twice in each round: for "orientation scanning" (the dynamic process of acquiring the final view) and for the "block view" (which visualizes the block site and is maintained for needle insertion/injection). Strong recommendations for inclusion were made if ≥75% of participants rated a structure as "definitely include" in any round. Weak recommendations were made if >50% of participants rated a structure as "definitely include" or "probably include" for all rounds (but the criterion for "strong recommendation" was never met). Thirty-six participants (94.7%) completed all rounds. 128 structures were reviewed; a "strong recommendation" is made for 35 structures on orientation scanning and 28 for the block view. A "weak recommendation" is made for 36 and 20 structures, respectively. This study provides recommendations on the core (minimum) set of anatomical structures to identify during ultrasound scanning for seven basic blocks in UGRA. They are intended to support consistent practice, empower non-experts using basic UGRA techniques, and standardize teaching and research.

Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anesthesia.

Ultrasound-guided regional anesthesia involves visualizing sono-anatomy to guide needle insertion and the perineural injection of local anesthetic. Anatomical knowledge and recognition of anatomical structures on ultrasound are known to be imperfect amongst anesthesiologists. 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. Unmodified ultrasound videos were presented side-by-side with AI-highlighted ultrasound videos. Experts rated the overall system performance, ascertained whether highlighting helped identify specific anatomical structures, and provided opinion on whether it would help confirm the correct ultrasound view to a less experienced practitioner. Two hundred and seventy-five assessments were performed (five videos contained inadequate views); mean highlighting scores ranged from 7.87 to 8.69 (out of 10). The Kruskal-Wallis H-test showed a statistically significant difference in the overall performance rating (χ2 [6] = 36.719, asymptotic p < 0.001); regions containing a prominent vascular landmark ranked most highly. AI-highlighting was helpful in identifying specific anatomical structures in 1330/1334 cases (99.7%) and for confirming the correct ultrasound view in 273/275 scans (99.3%). These data demonstrate the clinical utility of an assistive AI system in aiding the identification of anatomical structures on ultrasound during ultrasound-guided regional anesthesia. Whilst further evaluation must follow, such technology may present an opportunity to enhance clinical practice and energize the important field of clinical anatomy amongst clinicians.

Arterial anatomy of the anterior abdominal wall: Ultrasound evaluation as a real-time guide to percutaneous instrumentation.

INTRODUCTION: Instrumenting the anterior abdominal wall carries a potential for vascular trauma. We previously assessed the presence, position, and size of the anterior abdominal wall superior and inferior (deep) epigastric arteries with computed tomography (CT). We now present a study using ultrasound (US) assessment of these arteries, to evaluate its use for real time guidance of percutaneous procedures involving the rectus sheath. MATERIALS AND METHODS: Twenty-four participants (mean age 67.9 ± 9 years, 15 M:9 F [62:38%]) were assessed with US at three axial planes on the anterior abdominal wall: transpyloric plane (TPP), umbilicus, and anterior superior iliac spine (ASIS). RESULTS: An artery was visible least frequently at the TPP (62.5 - 45.8%), compared with the umbilicus (95.8-100%) and ASIS (100%), on the left, χ2 (2) = 20.571; p < .001, and right, χ2 (2) = 27.842; p < .001, with a moderate strength association (Cramer's V = 0.535 [left] and 0.622 [right]). Arteries were most commonly observed within the rectus abdominis muscle at the level of the TPP and umbilicus, but posterior to the muscle at the level of the ASIS (95.8-100%). As with the CT study, the inferior epigastric artery was observed to be larger in diameter, start more laterally, and move medially as it coursed superiorly. CONCLUSIONS: These data corroborate our previous results and suggest that the safest level to instrument the rectus sheath (with respect to vascular anatomy) is at the TPP. Such information may be particularly relevant to anesthetists performing rectus sheath block and surgeons during laparoscopic port insertion.

A marking of the cricothyroid membrane with extended neck returns to correct position after neck manipulation and repositioning.

BACKGROUND: Emergency front of neck airway access by anaesthetists carries a high failure rate and it is recommended to identify the cricothyroid membrane before induction of anaesthesia in patients with a predicted difficult airway. We have investigated whether a marking of the cricothyroid membrane done in the extended neck position remains correct after the patient's neck has been manipulated and subsequently repositioned. METHODS: The subject was first placed in the extended head and neck position and had the cricothyroid membrane identified and marked with 3 methods, palpation, 'laryngeal handshake' and ultrasonography and the distance from the suprasternal notch to the cricothyroid membrane was measured. The subject then moved off the table and sat on a chair and subsequently returned to the extended neck position and examinations were repeated. RESULTS: Skin markings of all 11 subjects lay within the boundaries of the cricothyroid membrane when the subject was repositioned back to the extended neck position and the median difference between the two measurements of the distance from the suprasternal notch was 0 mm (range 0-2 mm). CONCLUSION: The cricothyroid membrane can be identified and marked with the subject in the extended neck position. Then the patient's position can be changed as needed, for example to the 'sniffing' neck position for conventional intubation. If a front of neck airway access is required during subsequent airway management, the patient can be returned expediently to the extended-neck position, and the marking of the centre of the membrane will still be in the correct place.

Ultrasound-Guided Regional Anaesthesia: Visualising the Nerve and Needle.

Regional anaesthesia involves targeting specific peripheral nerves with local anaesthetic. It facilitates the delivery of anaesthesia and analgesia to an increasingly complex, elderly and co-morbid patient population. Regional anaesthesia practice has been transformed by the use of ultrasound, which confers advantages such as accuracy of needle placement, visualisation of local anaesthetic spread, avoidance of intraneural injection and the ability to accommodate for anatomical variation.An US beam is generated by the application of electrical current to an array of piezoelectric crystals, causing vibration and consequential production of high-frequency sound waves. The sound energy is reflected at tissue interfaces, detected by the piezoelectric crystals in the ultrasound probe, and most frequently displayed as a 2D image.Optimising image acquisition involves selection of the appropriate US frequency: this represents a trade-off between image resolution (better with high frequency) and tissue penetration/beam attenuation (better with low frequency). Altering alignment, rotation and tilt of the probe is often required to optimise the view as nerves are best visualised when the ultrasound beam is directly perpendicular to their fibres. Adjusting the focus, depth, and gain (brightness) of the image display can also help in this matter.Three key challenges exist in regional anaesthesia; image optimisation, image interpretation (nerve visualisation) and needle visualisation. There are characteristic sonographic appearances of the nerve structures for peripheral nerve blocks, as discussed in this chapter, and the above techniques can be used to enhance their appearance. Much research has been done, and is ongoing, with the aim of improving needle visualisation; this is also reviewed. Image interpretation requires the application of anatomical knowledge and understanding of the typical sonographic appearance of different tissues (as well as the needle). Years of practice are required to attain expertise, although it is hoped that continuing advances in nerve and needle visualisation, as described in this chapter, will expedite that process.