Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia

Artificial intelligence and robotics in regional anesthesia

Artificial intelligence (AI) technology is vital for practitioners to incorporate AI and robotics in day-to-day regional anesthesia practice. Recent literature is encouraging on its applications in regional anesthesia, but the data are limited. AI can help us identify and guide the needle tip precisely to the location. This may help us reduce the time, improve precision, and reduce the associated side effects of improper distribution of drugs. In this article, we discuss the potential roles of AI and robotics in regional anesthesia.

Optimizing peripheral regional anaesthesia: strategies for single shot and continuous blocks

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.

The educational impact of technology-enhanced learning in regional anaesthesia: a scoping review

BACKGROUND

Effective training in regional anaesthesia (RA) is paramount to ensuring widespread competence. Technology-based learning has assisted other specialties in achieving more rapid procedural skill acquisition. If applicable to RA, technology-enhanced training has the potential to provide an effective learning experience and to overcome barriers to RA training. We review the current evidence base for use of innovative technologies in assisting learning of RA.

METHODS

Using scoping review methodology, three databases (MEDLINE, Embase, and Web of Science) were searched, identifying 158 relevant citations. Citations were screened against defined eligibility criteria with 27 studies selected for inclusion. Data relating to study details, technological learning interventions, and impact on learner experience were extracted and analysed.

RESULTS

Seven different technologies were used to train learners in RA: artificial intelligence, immersive virtual reality, desktop virtual reality, needle guidance technology, robotics, augmented reality, and haptic feedback devices. Of 27 studies, 26 reported a positive impact of technology-enhanced RA training, with different technologies offering benefits for differing components of RA training. Artificial intelligence improved sonoanatomical knowledge and ultrasound skills for RA, whereas needle guidance technologies enhanced confidence and improved needling performance, particularly in novices. Immersive virtual reality allowed more rapid acquisition of needling skills, but its functionality was limited when combined with haptic feedback technology. User friendly technologies enhanced participant experience and improved confidence in RA; however, limitations in technology-assisted RA training restrict its widespread use.

CONCLUSIONS

Technology-enhanced RA training can provide a positive and effective learning experience, with potential to reduce the steep learning curve associated with gaining RA proficiency. A combined approach to RA education, using both technological and traditional approaches, should be maintained as no single method has been shown to provide comprehensive RA training.

The Future of Artificial Intelligence in Surgery

Until recently, innovations in surgery were largely represented by extensions or augmentations of the surgeon's perception. This includes advancements such as the operating microscope, tumor fluorescence, intraoperative ultrasound, and minimally invasive surgical instrumentation. However, introducing artificial intelligence (AI) into the surgical disciplines represents a transformational event. Not only does AI contribute substantively to enhancing a surgeon's perception with such methodologies as three-dimensional anatomic overlays with augmented reality, AI-improved visualization for tumor resection, and AI-formatted endoscopic and robotic surgery guidance. What truly makes AI so different is that it also provides ways to augment the surgeon's cognition. By analyzing enormous databases, AI can offer new insights that can transform the operative environment in several ways. It can enable preoperative risk assessment and allow a better selection of candidates for procedures such as organ transplantation. AI can also increase the efficiency and throughput of operating rooms and staff and coordinate the utilization of critical resources such as intensive care unit beds and ventilators. Furthermore, AI is revolutionizing intraoperative guidance, improving the detection of cancers, permitting endovascular navigation, and ensuring the reduction in collateral damage to adjacent tissues during surgery (e.g., identification of parathyroid glands during thyroidectomy). AI is also transforming how we evaluate and assess surgical proficiency and trainees in postgraduate programs. It offers the potential for multiple, serial evaluations, using various scoring systems while remaining free from the biases that can plague human supervisors. The future of AI-driven surgery holds promising trends, including the globalization of surgical education, the miniaturization of instrumentation, and the increasing success of autonomous surgical robots. These advancements raise the prospect of deploying fully autonomous surgical robots in the near future into challenging environments such as the battlefield, disaster areas, and even extraplanetary exploration. In light of these transformative developments, it is clear that the future of surgery will belong to those who can most readily embrace and harness the power of AI.

Regional Anaesthesia, Contemporary Techniques, and Associated Advancements: A Narrative Review

In particular, the application of regional anaesthesia techniques in existing medicine can be characterized as experiencing regular changes in recent decades. It is useful for obtaining accurate and efficient pain management solutions, from the basic spinal and epidural blocks to the novel ultrasound nerve blocks and constant catheter procedures. These advancements do enhance not only the value of the perioperative period but also the patient's rated optimization as enhancing satisfaction, better precision, and the safety of nerve block placement. The use of ultrasound technology makes it even easier to determine the proper positioning of the needle and to monitor nerve block placement. Moreover, the duration and efficiency of regional anaesthesia are being enhanced by state-of-the-art approaches, which come in the form of liposomal bupivacaine, and better recovery plans and protocols, which shorten recovery time and decrease the number of hospital days. As these methods develop further, more improvements in the safety, efficacy, and applicability of regional anaesthesia in contemporary medicine are anticipated through continued research and innovation.

A progress of research on the application of fascial plane blocks in surgeries and their future direction: a review article

Fascial plane blocks (FPBs) are gaining popularity in clinical settings owing to their improved analgesia when combined with either traditional regional anesthesia or general anesthesia during the perioperative phase. The scope of study on FPBs has substantially increased over the past 20 years, yet the exact mechanism, issues linked to the approaches, and direction of future research on FPBs are still up for debate. Given that it can be performed at all levels of the spine and provides analgesia to most areas of the body, the erector spinae plane block, one of the FPBs, has been extensively studied for chronic rational pain, visceral pain, abdominal surgical analgesia, imaging, and anatomical mechanisms. This has led to the contention that the erector spinae plane block is the ultimate Plan A block. Yet even though the future of FPBs is promising, the unstable effect, the probability of local anesthetic poisoning, and the lack of consensus on the definition and assessment of the FPB's success are still the major concerns. In order to precisely administer FPBs to patients who require analgesia in this condition, an algorithm that uses artificial intelligence is required. This algorithm will assist healthcare professionals in practicing precision medicine.

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.

Variability between human experts and artificial intelligence in identification of anatomical structures by ultrasound in regional anaesthesia: a framework for evaluation of assistive artificial intelligence

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.

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.

Latest Advances in Regional Anaesthesia

Training and expertise in regional anaesthesia have increased significantly in tandem with increased interest over the past two decades. This review outlines the most recent advances in regional anaesthesia and focuses on novel areas of interest including fascial plane blocks. Pharmacological advances in the form of the prolongation of drug duration with liposomal bupivacaine are considered. Neuromodulation in the context of regional anaesthesia is outlined as a potential future direction. The growing use of regional anaesthesia outside of the theatre environment and current thinking on managing the rebound plane after regional block regression are also discussed. Recent relevant evidence is summarised, unanswered questions are outlined, and priorities for ongoing investigation are suggested.

Beyond the Echo: The Evolution and Revolution of Ultrasound in Anesthesia

This article explores the evolving role of ultrasound technology in anesthesia. Ultrasound emerged decades ago, offering clinicians noninvasive, economical, radiation-free, and real-time imaging capabilities. It might seem that such an old technology with apparent limitations might have had its day, but this review discusses both the current applications of ultrasound (in nerve blocks, vascular access, and airway management) and then, more speculatively, shows how integration of advanced ultrasound modalities such as contrast-enhanced imaging with virtual reality (VR), or nanotechnology can alter perioperative patient care. This article will also explore the potential of robotics and artificial intelligence (AI) in augmenting ultrasound-guided anesthetic procedures and their implications for medical practice and education.

Artificial intelligence in regional anaesthesia

Ultrasound-guided regional anaesthesia is used to facilitate the real-time performance of the regional block, increase the block success and reduce the complication rate. Artificial intelligence (AI) has been studied in many medical disciplines with high success rates, especially radiology. The purpose of this article was to review the evolution of AI in regional anaesthesia. The role of AI is to identify and optimise the sonography image, display the target, guide the practitioner to advance the needle tip to the intended target and inject the local anaesthetic. AI supports non-experts in training and clinical practice and experts in teaching ultrasound-guided regional anaesthesia.

Decision-making in anesthesiology: will artificial intelligence make intraoperative care safer?

PURPOSE OF REVIEW

This article explores the impact of recent applications of artificial intelligence on clinical anesthesiologists' decision-making.

RECENT FINDINGS

Naturalistic decision-making, a rich research field that aims to understand how cognitive work is accomplished in complex environments, provides insight into anesthesiologists' decision processes. Due to the complexity of clinical work and limits of human decision-making (e.g. fatigue, distraction, and cognitive biases), attention on the role of artificial intelligence to support anesthesiologists' decision-making has grown. Artificial intelligence, a computer's ability to perform human-like cognitive functions, is increasingly used in anesthesiology. Examples include aiding in the prediction of intraoperative hypotension and postoperative complications, as well as enhancing structure localization for regional and neuraxial anesthesia through artificial intelligence integration with ultrasound.

SUMMARY

To fully realize the benefits of artificial intelligence in anesthesiology, several important considerations must be addressed, including its usability and workflow integration, appropriate level of trust placed on artificial intelligence, its impact on decision-making, the potential de-skilling of practitioners, and issues of accountability. Further research is needed to enhance anesthesiologists' clinical decision-making in collaboration with artificial intelligence.

Artificial intelligence evaluation of focused assessment with sonography in trauma

BACKGROUND

The focused assessment with sonography in trauma (FAST) is a widely used imaging modality to identify the location of life-threatening hemorrhage in a hemodynamically unstable trauma patient. This study evaluates the role of artificial intelligence in interpretation of the FAST examination abdominal views, as it pertains to adequacy of the view and accuracy of fluid survey positivity.

METHODS

Focused assessment with sonography for trauma examination images from 2015 to 2022, from trauma activations, were acquired from a quaternary care level 1 trauma center with more than 3,500 adult trauma evaluations, annually. Images pertaining to the right upper quadrant and left upper quadrant views were obtained and read by a surgeon or radiologist. Positivity was defined as fluid present in the hepatorenal or splenorenal fossa, while adequacy was defined by the presence of both the liver and kidney or the spleen and kidney for the right upper quadrant or left upper quadrant views, respectively. Four convolutional neural network architecture models (DenseNet121, InceptionV3, ResNet50, Vgg11bn) were evaluated.

RESULTS

A total of 6,608 images, representing 109 cases were included for analysis within the "adequate" and "positive" data sets. The models relayed 88.7% accuracy, 83.3% sensitivity, and 93.6% specificity for the adequate test cohort, while the positive cohort conferred 98.0% accuracy, 89.6% sensitivity, and 100.0% specificity against similar models. Augmentation improved the accuracy and sensitivity of the positive models to 95.1% accurate and 94.0% sensitive. DenseNet121 demonstrated the best accuracy across tasks.

CONCLUSION

Artificial intelligence can detect positivity and adequacy of FAST examinations with 94% and 97% accuracy, aiding in the standardization of care delivery with minimal expert clinician input. Artificial intelligence is a feasible modality to improve patient care imaging interpretation accuracy and should be pursued as a point-of-care clinical decision-making tool.

LEVEL OF EVIDENCE

Diagnostic Test/Criteria; Level III.

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.

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.

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.

Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision

The number of hip fractures per year worldwide is estimated to reach 6 million by the year 2050. Despite the many advantages of regional blockades when managing pain from such a fracture, these are used to a lesser extent than general analgesia. One reason is that the opportunities for training and obtaining clinical experience in applying nerve blocks can be a challenge in many clinical settings. Ultrasound image guidance based on artificial intelligence may be one way to increase nerve block success rate. We propose an approach using a deep learning semantic segmentation model with U-net architecture to identify the femoral nerve in ultrasound images. The dataset consisted of 1410 ultrasound images that were collected from 48 patients. The images were manually annotated by a clinical professional and a segmentation model was trained. After training the model for 350 epochs, the results were validated with a 10-fold cross-validation. This showed a mean Intersection over Union of 74%, with an interquartile range of 0.66-0.81.

Examining the impact perceptual learning artificial-intelligence-based on the incidence of paresthesia when performing the ultrasound-guided popliteal sciatic block: simulation-based randomized study

OBJECTIVE

To explore the impact of artificial-intelligence perceptual learning when performing the ultrasound-guided popliteal sciatic block.

METHODS

This simulation-based randomized study enrolled residents who underwent ultrasound-guided sciatic nerve block training at the Department of Anesthesiology of Beijing Jishuitan Hospital between January 2022 and February 2022. Residents were randomly divided into a traditional teaching group and an AI teaching group. All residents attended the same nerve block theory courses, while those in the AI teaching group participated in training course using an AI-assisted nerve identification system based on a convolutional neural network instead of traditional training.

RESULTS

A total of 40 residents were included. The complication rates of paresthesia during puncture in the first month of clinical sciatic nerve block practice after training were significantly lower in the AI teaching group than in the traditional teaching group [11 (4.12%) vs. 36 (14.06%), P = 0.000093]. The rates of paresthesia/pain during injection were significantly lower in the AI teaching group than in the traditional teaching group [6 (2.25%) vs. 17 (6.64%), P = 0.025]. The Assessment Checklist for Ultrasound-Guided Regional Anesthesia (32 ± 3.8 vs. 29.4 ± 3.9, P = 0.001) and nerve block self-rating scores (7.53 ± 1.62 vs. 6.49 ± 1.85, P < 0.001) were significantly higher in the AI teaching group than in the traditional teaching group. There were no significant differences in the remaining indicators.

CONCLUSION

The inclusion of an AI-assisted nerve identification system based on convolutional neural network as part of the training program for ultrasound-guided sciatic nerve block via the popliteal approach may reduce the incidence of nerve paresthesia and this might be related to improved perceptual learning.

CLINICAL TRIAL

CHiCTR2200055115 , registered on 1/ January /2022.

Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

BACKGROUND

Regional anesthesia is increasingly used in acute postoperative pain management. Ultrasound has been used to facilitate the performance of the regional block, increase the percentage of successfully performed procedures and reduce the complication rate. Artificial intelligence (AI) has been studied in many medical disciplines with achieving high success, especially in radiology. The purpose of this review was to review the evidence on the application of artificial intelligence for optimization and interpretation of the sonographic image, and visualization of needle advancement and injection of local anesthetic.

METHODS

To conduct this scoping review, we followed the PRISMA-S guidelines. We included studies if they met the following criteria: (1) Application of Artificial intelligence-assisted in ultrasound-guided regional anesthesia; (2) Any human subject (of any age), object (manikin), or animal; (3) Study design: prospective, retrospective, RCTs; (4) Any method of regional anesthesia (epidural, spinal anesthesia, peripheral nerves); (5) Any anatomical localization of regional anesthesia (any nerve or plexus) (6) Any methods of artificial intelligence; (7) Settings: Any healthcare settings (Medical centers, hospitals, clinics, laboratories.

RESULTS

The systematic searches identified 78 citations. After the removal of the duplicates, 19 full-text articles were assessed; and 15 studies were eligible for inclusion in the review.

CONCLUSIONS

AI solutions might be useful in anatomical landmark identification, reducing or even avoiding possible complications. AI-guided solutions can improve the optimization and interpretation of the sonographic image, visualization of needle advancement, and injection of local anesthetic. AI-guided solutions might improve the training process in UGRA. Although significant progress has been made in the application of AI-guided UGRA, randomized control trials are still missing.

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.

Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review

The physiological and neuroregulatory mechanism of propofol is largely based on very limited knowledge. It is one of the important puzzling issues in anesthesiology and is of great value in both scientific and clinical fields. It is acknowledged that neural networks which are comprised of a number of neural circuits might be involved in the anesthetic mechanism. However, the mechanism of this hypothesis needs to be further elucidated. With the progress of artificial intelligence, it is more likely to solve this problem through using artificial neural networks to perform temporal waveform data analysis and to construct biophysical computational models. This review focuses on current knowledge regarding the anesthetic mechanism of propofol, an intravenous general anesthetic, by constructing biophysical computational models.

A paired comparison of nerve dimensions using B-Mode ultrasound and shear wave elastography during regional anaesthesia

Introduction:

Shear wave elastography (SWE) presents nerves in colour, but the dimensions of its colour maps have not been validated with paired B-Mode nerve images. Our primary objective was to define the bias and limits of agreement of SWE with B-Mode nerve diameter. Our secondary objectives were to compare nerve area and shape, and provide a clinical standard for future application of new colour imaging technologies such as artificial intelligence.

Materials and Methods:

Eleven combined ultrasound-guided regional nerve blocks were conducted using a dual-mode transducer. Two raters outlined nerve margins on 110 paired B-Mode and SWE images every second for 20 s before and during injection. Bias and limits of agreement were plotted on Bland-Altman plots. We hypothesized that the bias of nerve diameter would be <2.5% and that the percent limits of agreement would lie ±0.67% (2 SD) of the bias.

Results:

There was no difference in the bias (95% confidence interval (CI) limits of agreement) of nerve diameter measurement, 0.01 (−0.14 to 0.16) cm, P = 0.85, equivalent to a 1.4% (−56.6% to 59.5) % difference. The bias and limits of agreement were 0.03 (−0.08 to 0.15) cm2, P = 0.54 for cross-sectional nerve area; and 0.02 (−0.03 to 0.07), P = 0.45 for shape. Reliability (ICC) between raters was 0.96 (0.94–0.98) for B-Mode nerve area and 0.91 (0.83–0.95) for SWE nerve area.

Conclusions:

Nerve diameter measurement from B-Mode and SWE images fell within a priori measures of bias and limits of agreement.

A Study on the Role of Intelligent Medical Simulation Systems in Teaching First Aid Competence in Anesthesiology

Anesthesiology is a subject with strong practicality and application. Undergraduate anesthesiology teaching needs to strike a balance between theoretical knowledge, clinical skill training, and clinical thinking development. Clinical probation and practice are an important part of undergraduate anesthesia teaching. Traditional clinical teaching uses real patients for demonstration and training, but as patients become more self-protective and less cooperative, there are not enough patients for clinical skill training. Simulation is to teach medical scenes in real life under the control of standardized technical guidelines and parameters. Since then, with the rapid development of computer technology, simulation technology and simulation teaching have been greatly developed and are more and more used in clinical teaching, skill evaluation, and scientific research. This study explores the effective methods of clinical teaching in anesthesiology by comparing the effectiveness of traditional teaching methods and simulation teaching methods in undergraduate clinical teaching. It is difficult to combine theory and practice in first aid, which does not allow them to directly receive and deal with emergency medical treatment and resuscitation. In China's current medical environment and patients' high demand for medical services, it is imperative to vigorously carry out simulated medical education. In the eastern part of Inner Mongolia, according to the advantages of teaching hospitals, our hospital took the lead in carrying out the simulation education project, which is still in the exploratory stage and not systematic enough. This study will help us to better carry out simulation teaching and improve the clinical skills of medical students in the future. Methods. The student group and class took the advanced simulator training as the experimental group, applied the advanced integrated simulator and other systems of the Norwegian company, referred to the international guidelines for cardiopulmonary resuscitation and cardiovascular first aid in 2005, and practiced in the emergency department during the clinical internship and “emergency clinical simulation training” course. The course includes basic life support, advanced life support, and comprehensive training of CPR (cardiopulmonary resuscitation) and endotracheal intubation. Results. The passing rate of simulated first aid practice was 94.4%; 100% of the students think it is necessary to set up the course, 91% of the students think it is practical, 91% of the students think the course content is reasonable and perfect, and 77%–100% of the students think the course has improved their first aid operation ability, comprehensive application of knowledge, and clinical thinking ability. Conclusion. Carrying out the course of “clinical simulated first aid training” through the advanced simulator system can effectively improve the interns' clinical first aid operation ability, teamwork ability, and self-confidence, improve the students' clinical thinking and judgment ability, and improve the service level to patients.

New insights into the evaluation of peripheral nerves lesions: a survival guide for beginners

PURPOSE

To perform a review of the physical basis of DTI and DCE-MRI applied to Peripheral Nerves (PNs) evaluation with the aim of providing readers the main concepts and tools to acquire these types of sequences for PNs assessment. The potential added value of these advanced techniques for pre-and post-surgical PN assessment is also reviewed in diverse clinical scenarios. Finally, a brief introduction to the promising applications of Artificial Intelligence (AI) for PNs evaluation is presented.

METHODS

We review the existing literature and analyze the latest evidence regarding DTI, DCE-MRI and AI for PNs assessment. This review is focused on a practical approach to these advanced sequences providing tips and tricks for implementing them into real clinical practice focused on imaging postprocessing and their current clinical applicability. A summary of the potential applications of AI algorithms for PNs assessment is also included.

RESULTS

DTI, successfully used in central nervous system, can also be applied for PNs assessment. DCE-MRI can help evaluate PN's vascularization and integrity of Blood Nerve Barrier beyond the conventional gadolinium-enhanced MRI sequences approach. Both approaches have been tested for PN assessment including pre- and post-surgical evaluation of PNs and tumoral conditions. AI algorithms may help radiologists for PN detection, segmentation and characterization with promising initial results.

CONCLUSION

DTI, DCE-MRI are feasible tools for the assessment of PN lesions. This manuscript emphasizes the technical adjustments necessary to acquire and post-process these images. AI algorithms can also be considered as an alternative and promising choice for PN evaluation with promising results.

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.

e-Learning in regional anesthesia

PURPOSE OF REVIEW

To explore the role and impact of e-learning technologies on regional anesthesia.

RECENT FINDINGS

21st century technologies, such as 'smart' medical appliances, personal computers, sophisticated apps, the ubiquitous Internet, and online 'e-learning' curricula, are having a powerful impact on anesthesia training: when we learn, what we learn, and how we learn. But is 'new' necessarily 'better'? The answer will result from the application of developments in IT technology through the current vision of architects of future anesthesia training programs. This narrative review aims to summarize the recent developments in anesthesia e-learning, and to forecast trends using regional anesthesia as an example.

SUMMARY

The review offers some recommendations to ensure that the blessings promised to human learning by this 'Brave New Cyberworld' do not become its nemesis.

The evolution of epistemological methodologies in anatomy: From antiquity to modern times

Present day scenario regarding epistemological methods in anatomy is in sharp contrast to the situation during ancient period. This study aimed to explore the evolution of epistemological methodologies in anatomy across centuries. In ancient times Egyptian embalmers acquired anatomical knowledge from handling human bodies and likewise anatomical studies in India involved human dissection. Ancient Greeks used theological principles-based methods, animal dissection and human dissection in practice of anatomy. Human dissection was also practiced in ancient China for gaining anatomical knowledge. Prohibition of human dissection led to use of animal dissection in ancient Rome and the trend continued in Europe through Middle Ages. Epistemological methods used by Muslim scholars during Middle Ages are not clearly chronicled. Human dissection returned as primary epistemological method in Renaissance Europe and empirical methods were reinstated after ancient period in human dissection during 16th century. The situation further improved with introduction of pragmatic experiment based approach during 17th century and autopsy-based methods during 18th century. Advances in anatomical knowledge continued with advent of microscope-based methods and emergence of anatomical sections in practice of human dissection in 19th century. Introduction of human observational studies, medical imaging, and molecular methods presented more options in terms of epistemological methods for investigating the human body during 20th century. Onset of 21st century has witnessed dominance of technology-based methods in anatomy. Limited emphasis on ethics in epistemological methodologies since antiquity is a dark aspect of otherwise an eventful evolutionary journey but recent developments are in positive direction.

The role for regional anesthesia in medical emergencies during deep space flight

As humanity presses the boundaries of space exploration and prepares for long-term interplanetary travel, including to Mars, advanced planning for the safety and health of the crewmembers requires a multidisciplinary approach. In particular, in the event of a survivable medical emergency requiring an interventional procedure or prolonged pain management, such as traumatic limb injury or rib fracture, anesthetic protocols that are both safe and straightforward to execute must be in place. In this daring discourse, we discuss particular considerations related to the use of regional techniques in space and present the rationale that regional anesthesia techniques may be the safest option in many medical emergencies encountered during prolonged space flight.

A real-time anatomy ıdentification via tool based on artificial ıntelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study

We aimed to assess the accuracy of an artificial intelligence (AI)-based real-time anatomy identification software specifically developed to ease image interpretation intended for ultrasound-guided peripheral nerve block (UGPNB). Forty healthy participants (20 women, 20 men) were enrolled to perform interscalene, supraclavicular, infraclavicular, and transversus abdominis plane (TAP) blocks under ultrasound guidance using AI software by anesthesiology trainees. During block practice by a trainee, once the software indicates 100% scan success of each block associated anatomic landmarks, both raw and labeled ultrasound images were saved, assessed, and validated using a 5-point scale by expert validators. When trainees reached 100% scan success, accuracy scores of the validators were noted. Correlation analysis was used whether the relationship (r) according to demographics (gender, age, and body mass index: BMI) and block type exist. The BMI (kg/m²) and age (year) of participants were 22.2 ± 3 and 32.2 ± 5.25, respectively. Assessment scores of validators for all blocks were similar in male and female individuals. Mean assessment scores of validators were not significantly different according to age and BMI except for TAP block, which was inversely correlated with age and BMI (p = 0.01). AI technology can successfully interpret anatomical structures in real-time sonography while assisting young anesthesiologists during UGPNB practice.

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.