Accuracy of Non-Invasive Imaging Techniques for the Diagnosis of MASH in Patients With MASLD: A Systematic Review.

BACKGROUND AND AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing public health problem. The secondary stage in MASLD is steatohepatitis (MASH), the co-existence of steatosis and inflammation, a leading cause of progression to fibrosis and mortality. MASH resolution alone improves survival. Currently, MASH diagnosis is via liver biopsy. This study sought to evaluate the accuracy of imaging-based tests for MASH diagnosis, which offer a non-invasive method of diagnosis. METHODS: Eight academic literature databases were searched and references of previous systematic reviews and included papers were checked for additional papers. Liver biopsy was used for reference standard. RESULTS: We report on 69 imaging-based studies. There were 31 studies on MRI, 27 on ultrasound, five on CT, 13 on transient elastography, eight on controlled attenuation parameter (CAP) and two on scintigraphy. The pathological definition of MASH was inconsistent, making it difficult to compare studies. 55/69 studies (79.71%) were deemed high-risk of bias as they had no preset thresholds and no validation. The two largest groups of imaging papers were on MRI and ultrasound. AUROCs were up to 0.93 for MRE, 0.90 for MRI, 1.0 for magnetic resonance spectroscopy (MRS) and 0.94 for ultrasound-based studies. CONCLUSIONS: Our study found that the most promising imaging tools are MRI techniques or ultrasound-based scores and confirmed there is potential to utilise these for MASH diagnosis. However, many publications are single studies without independent prospective validation. Without this, there is no clear imaging tool or score currently available that is reliably tested to diagnose MASH.

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

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

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

BACKGROUND: Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia. METHODS: A literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed. RESULTS: In total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016-17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation. CONCLUSIONS: There is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption.

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.

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.

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.

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.

Arterial Anatomy of the Anterior Abdominal Wall: Evidence-Based Safe Sites for Instrumentation Based on Radiological Analysis of 100 Patients.

Multiple medical interventions require percutaneous instrumentation of the anterior abdominal wall, all of which carry a potential for vascular trauma. We assessed the presence, position, and size of the anterior abdominal wall superior and inferior (deep) epigastric arteries to determine the safest site with respect to vascular anatomy of the rectus sheath. In a review of 100 arterial phase, contrast-enhanced abdominal computed tomography scans, anterior abdominal wall arteries were assessed bilaterally at three axial planes: transpyloric, umbilicus, and anterior superior iliac spine (ASIS). The mean age of patients was 69.2 years (SD ± 15), with 62 male and 38 female. An artery was visible least frequently at the transpyloric plane (5%), compared with the umbilicus (72-79%) and ASIS (93-96%), on the left (χ2 (4) = 207.272; P < 0.001) and right (χ2 (4) = 198.553; P < 0.001), with a moderate strength association (Cramer's V = 0.588 (left) and 0.575 (right)). The arteries were most commonly observed within the rectus abdominis muscle at the level of the umbilicus and ASIS on both sides (62-68%). The inferior epigastric artery was observed to be larger in diameter, start more laterally, and move medially as it travelled superiorly. These data suggest that the safest site to instrument the rectus sheath, with respect to vascular anatomy, is at the transpyloric plane. This information on anatomical variation of the anterior abdominal wall vasculature may be of particular interest to anesthetists performing rectus sheath block and surgeons during laparoscopic port insertion. Clin. Anat. 33:350-354, 2020. © 2019 Wiley Periodicals, Inc.

Identifying the emergence of the superficial peroneal nerve through deep fascia on ultrasound and by dissection: Implications for regional anesthesia in foot and ankle surgery.

Regional anesthesia relies on a sound understanding of anatomy and the utility of ultrasound in identifying relevant structures. We assessed the ability to identify the point at which the superficial peroneal nerve (SPN) emerges through the deep fascia by ultrasound on 26 volunteers (mean age 27.85 years ± 13.186; equal male: female). This point was identified, characterized in relation to surrounding bony landmarks (lateral malleolus and head of the fibula), and compared to data from 16 formalin-fixed human cadavers (mean age 82.88 years ± 6.964; equal male: female). The SPN was identified bilaterally in all subjects. On ultrasound it was found to pierce the deep fascia of the leg at a point 0.31 (±0.066) of the way along a straight line from the lateral malleolus to the head of the fibula (LM-HF line). This occurred on or anterior to the line in all cases. Dissection of cadavers found this point to be 0.30 (±0.062) along the LM-HF line, with no statistically significant difference between the two groups (U = 764.000; exact two-tailed P = 0.534). It was always on or anterior to the LM-HF line, anterior by 0.74 cm (±0.624) on ultrasound and by 1.51 cm (±0.509) during dissection. This point was significantly further anterior to the LM-HF line in cadavers (U = 257.700, exact two-tailed P < 0.001). Dissection revealed the nerve to divide prior to emergence in 46.88% (n = 15) limbs, which was not identified on ultrasound (although not specifically assessed). Such information can guide clinicians when patient factors (e.g., obesity and peripheral edema) make ultrasound-guided nerve localization more technically challenging. Clin. Anat. 32:390-395, 2019. © 2019 Wiley Periodicals, Inc.

Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia.

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: www.clinicaltrials.govNCT05583032.

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.

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.