Fig 1 - available via license: Creative Commons Attribution 4.0 International
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Examples of ScanNav Anatomy Peripheral Nerve Block colour overlay. ISB (interscalene level brachial plexus block): AS, anterior scalene; C5, C5 nerve root; C6, C6 nerve root; MS, middle scalene. AxBP (axillary level brachial plexus block): AA, axillary artery; AV, axillary vein; CT, conjoint (common) tendon of latissimus dorsi/teres major; McN, musculocutaneous nerve; MN, median nerve; RN, radial nerve; UN, ulnar nerve. ESP (erector spinae block): ESM, erector spinae muscle group (and overlying muscles); TP, transverse process. RSB (rectus sheath block): P, peritoneum; RA, rectus abdominis; RSa, rectus sheath (anterior layer); RSp, rectus sheath (posterior layer). ACB (adductor canal block): FA, femoral artery; SaN, saphenous nerve; SM, sartorius muscle. SNB (popliteal level sciatic nerve block): CPN, common peroneal (fibular) nerve; TN, tibial nerve.
Source publication
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 explorat...
Contexts in source publication
Context 1
... We and others have reported the use of assistive AI for ultrasound scanning in UGRA. 7,12 The device in our studies, ScanNav Anatomy Peripheral Nerve Block (ScanNav TM ; Intelligent Ultrasound, Cardiff, UK), uses DL techniques to create a real-time colour overlay of B-mode ultrasound to highlight salient structures (Fig 1 and Supplementary File A online video). The system aims to draw attentional gaze to the area of interest, to aid in acquisition of the correct ultrasound view for specific nerve blocks and in the correct identification of structures on that view. ...
Context 2
... scanning was performed using a linear probe on PX, SII, and X-Porte SonoSite ultrasound machines (Fujifilm SonoSite, Bothell, WA, USA). ScanNav TM was connected to the high-definition multimedia interface output of each ultrasound machine, and stationed next to the machine in question, to display the same ultrasound image but with the associated real-time colour overlay (Fig 1). This device has been given regulatory approval for clinical use in Europe (April 2021) and data collected in this study have been submitted to the notifiable body as part of post-market clinical follow-up activities. ...
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Citations
... In a study involving non-experts in ultrasoundguided (USG) peripheral anesthesia, teaching sessions on USG scanning for peripheral nerve blocks (PNB) were conducted. 3 Half of the participants performed scans with the assistance of AI, while the other half did so without AI. The author observed that the use of AI led to improved acquisition and interpretation of USG images in non-experts. ...
... The study suggests that the future use of the ScanNav Anatomy Peripheral Nerve Block system could enhance the performance of novices in USG-guided procedures. 3 The technology highlights ten vital areas of sono-anatomy pertinent to ultrasound-guided regional anesthesia, including Supraclavicular, Interscalene, Axillary, and Brachial plexus for the upper limb, and Erector Spinae Plane, Rectus Sheath, Femoral nerve, Adductor canal, Popliteal, and Supra-inguinal Fascia Iliaca for the lower limb. 3,4 The interpretation and acquisition of sono-anatomical images, a crucial skill in ultrasound-guided regional anesthesia, is known to vary even among experts. ...
... 3 The technology highlights ten vital areas of sono-anatomy pertinent to ultrasound-guided regional anesthesia, including Supraclavicular, Interscalene, Axillary, and Brachial plexus for the upper limb, and Erector Spinae Plane, Rectus Sheath, Femoral nerve, Adductor canal, Popliteal, and Supra-inguinal Fascia Iliaca for the lower limb. 3,4 The interpretation and acquisition of sono-anatomical images, a crucial skill in ultrasound-guided regional anesthesia, is known to vary even among experts. AI training in this technology is specifically designed to train needle-probe orientation and anatomy-elucidation skills before performing on patients. ...
Artificial Intelligence (AI) has attained new frontiers in every field, including anaesthesiology. The application of the algorithm, empowerment of machines to analyze and solve troubles, word/ object perception, and governing decision-making outlines the basic concept of AI definition.
... In a study involving non-experts in ultrasoundguided (USG) peripheral anesthesia, teaching sessions on USG scanning for peripheral nerve blocks (PNB) were conducted. 3 Half of the participants performed scans with the assistance of AI, while the other half did so without AI. The author observed that the use of AI led to improved acquisition and interpretation of USG images in non-experts. ...
... The study suggests that the future use of the ScanNav Anatomy Peripheral Nerve Block system could enhance the performance of novices in USG-guided procedures. 3 The technology highlights ten vital areas of sono-anatomy pertinent to ultrasound-guided regional anesthesia, including Supraclavicular, Interscalene, Axillary, and Brachial plexus for the upper limb, and Erector Spinae Plane, Rectus Sheath, Femoral nerve, Adductor canal, Popliteal, and Supra-inguinal Fascia Iliaca for the lower limb. 3,4 The interpretation and acquisition of sono-anatomical images, a crucial skill in ultrasound-guided regional anesthesia, is known to vary even among experts. ...
... 3 The technology highlights ten vital areas of sono-anatomy pertinent to ultrasound-guided regional anesthesia, including Supraclavicular, Interscalene, Axillary, and Brachial plexus for the upper limb, and Erector Spinae Plane, Rectus Sheath, Femoral nerve, Adductor canal, Popliteal, and Supra-inguinal Fascia Iliaca for the lower limb. 3,4 The interpretation and acquisition of sono-anatomical images, a crucial skill in ultrasound-guided regional anesthesia, is known to vary even among experts. AI training in this technology is specifically designed to train needle-probe orientation and anatomy-elucidation skills before performing on patients. ...
Artificial Intelligence (AI) has attained new frontiers in
every field, including anaesthesiology. The application of
the algorithm, empowerment of machines to analyze and
solve troubles, word/ object perception, and governing
decision-making outlines the basic concept of AI
definition. 1
... Participants were stratified into four groups of five based on their experience in UGRA: novice (within the first 18 months of anaesthetic training), early career (from 18 months to 6 years into anaesthetic training), experienced anaesthetists (advanced trainees and non-expert consultants), and UGRA experts. In accordance with recent related studies, the definition of an expert was a consultant anaesthetist in the UK who met at least three of the following criteria [11][12][13]: (i) completed fellowship training in UGRA; (ii) holds a qualification related to UGRA (e.g., EDRA, higher degree, or equivalent); (iii) regularly delivers direct clinical care using UGRA, including for 'awake' surgery; (iv) regularly teaches UGRA, including regularly performing/teaching advanced techniques [14]. ...
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.
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.
Objectives
Ultrasound-guided regional anesthesia (UGRA) relies on acquiring and interpreting an appropriate view of sonoanatomy. Artificial intelligence (AI) has the potential to aid this by applying a color overlay to key sonoanatomical structures.
The primary aim was to determine whether an AI-generated color overlay was associated with a difference in participants’ ability to identify an appropriate block view over a 2-month period after a standardized teaching session (as judged by a blinded assessor). Secondary outcomes included the ability to identify an appropriate block view (unblinded assessor), global rating score and participant confidence scores.
Design
Randomized, partially blinded, prospective cross-over study.
Setting
Simulation scans on healthy volunteers. Initial assessments on 29 November 2022 and 30 November 2022, with follow-up on 25 January 2023 – 27 January 2023.
Participants
57 junior anesthetists undertook initial assessments and 51 (89.47%) returned at 2 months.
Intervention
Participants performed ultrasound scans for six peripheral nerve blocks, with AI assistance randomized to half of the blocks. Cross-over assignment was employed for 2 months.
Main outcome measures
Blinded experts assessed whether the block view acquired was acceptable (yes/no). Unblinded experts also assessed this parameter and provided a global performance rating (0–100). Participants reported scan confidence (0–100).
Results
AI assistance was associated with a higher rate of appropriate block view acquisition in both blinded and unblinded assessments (p=0.02 and <0.01, respectively). Participant confidence and expert rating scores were superior throughout (all p<0.01).
Conclusions
Assistive AI was associated with superior ultrasound scanning performance 2 months after formal teaching. It may aid application of sonoanatomical knowledge and skills gained in teaching, to support delivery of UGRA beyond the immediate post-teaching period.
Trial registration number
NCT05583032 .
The recent advancement in regional anesthesia (RA) has been largely attributed to ultrasound technology. However, the safety and efficiency of ultrasound-guided nerve blocks depend upon the skill and experience of the performer. Even with adequate training, experience, and knowledge, human-related limitations such as fatigue, failure to recognize the correct anatomical structure, and unintentional needle or probe movement can hinder the overall effectiveness of RA. The amalgamation of artificial intelligence (AI) to RA practice has promised to override these human limitations. Machine learning, an integral part of AI can improve its performance through continuous learning and experience, like the human brain. It enables computers to recognize images and patterns specifically useful in anatomic structure identification during the performance of RA. AI can provide real-time guidance to clinicians by highlighting important anatomical structures on ultrasound images, and it can also assist in needle tracking and accurate depo-sition of local anesthetics. The future of RA with AI integration appears promising , yet obstacles such as device malfunction, data privacy, regulatory barriers, and cost concerns can deter its clinical implementation. The current mini review deliberates the current application, future direction, and barrier to the application of AI in RA practice.
Purpose
The present study reviews the available scientific literature on artificial intelligence (AI)‐assisted ultrasound‐guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and postprocedural outcomes.
Methods
A literature search was performed on 19 September 2023, using the Medline, EMBASE, CINAHL, Cochrane Library and Google Scholar databases by experts in electronic searching. All study designs were considered with no restrictions regarding patient characteristics or cohort size. Outcomes assessed included the accuracy of AI‐model tracking, success at the first attempt, differences in outcomes between AI‐assisted and unassisted UGRA, operator feedback and case‐report data.
Results
A joint adaptive median binary pattern (JAMBP) has been applied to improve the tracking procedure, while a particle filter (PF) is involved in feature extraction. JAMBP combined with PF was most accurate on all images for landmark identification, with accuracy scores of 0.83, 0.93 and 0.93 on original, preprocessed and filtered images, respectively. Evaluation of first‐attempt success of spinal needle insertion revealed first‐attempt success in most patients. When comparing AI application versus UGRA alone, a significant statistical difference (p < 0.05) was found for correct block view, correct structure identification and decrease in mean injection time, needle track adjustments and bone encounters in favour of having AI assistance. Assessment of operator feedback revealed that expert and nonexpert operator feedback was overall positive.
Conclusion
AI appears promising to enhance UGRA as well as to positively influence operator training. AI application of UGRA may improve the identification of anatomical structures and provide guidance for needle placement, reducing the risk of complications and improving patient outcomes.
Level of Evidence
Level IV.
Purpose of review
Regional anaesthesia is increasingly prominent within anaesthesia, offering alternative analgesic options amidst concerns over opioid-based analgesia. Since Halsted's initial description, the field has burgeoned, with ultrasound visualization revolutionizing local anaesthetic spread assessment, leading to the development of numerous novel techniques. The benefits of regional anaesthesia have gained increasing evidence to support their application, leading to changes within training curricula. Consequently, regional anaesthesia is at a defining moment, embracing the development of core skills for the general anaesthesiologist, whilst also continuing the advancement of the specialty.
Recent findings
Recent priority setting projects have focussed attention on key aspects of regional anaesthesia delivery, including pain management, conduct and efficacy, education, and technological innovation. Developments in our current understanding of anatomy and pharmacology, combined with strategies for optimizing the conduct and maximizing efficacy of techniques, minimizing complications, and enhancing outcomes are explored. In addition, advancements in education and training methodologies and the integration of progress in novel technologies will be reviewed.
Summary
This review highlights recent scientific advances in optimizing both single-shot and continuous peripheral regional anaesthesia techniques. By synthesizing these developments, this review offers valuable insights into the evolving landscape of regional anaesthesia, aiming to improve clinical practice and patient care.
