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The proportion of the nerve location on the body surface in the scapula region of the traditional method and that under ultrasound.
Source publication
In order to discuss the clinical characteristics of patients with scapular fracture, deep learning model was adopted in ultrasound images of patients to locate the anesthesia point of patients during scapular fracture surgery treated with the regional nerve block. 100 patients with scapular fracture who were hospitalized for emergency treatment in...
Citations
... The majority of studies were conducted in China (n = 24, 37%) 12,15,27,29,30,[37][38][39][40][41]43,[59][60][61][62][63][64][65][66]68,69,[71][72][73] , USA (n = 12, 18%) 16,19,22,25,28,32,42,51,55,56,67,70 and Japan (n = 5, 8%) 9,24,31,33,50 . There were 4 international multicentre studies conducted across European sites 11,13,34,44 and 10 studies performed within individual European countries: France (n = 3) 18,36,58 , Italy (n = 2) 53,54 , Spain (n = 2) 21,47 , England (n = 1) 26 , Germany (n = 1) 48 and Denmark (n = 1) 14 . ...
The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.
... Since it is a practical, safe, efficient, and affordable option, ultrasonography is used in many anesthesiology techniques, however, US images are frequently challenging to interpret because they are frequently affected by artifacts and shadowing. Liu et all [20] design a deep learning model to guide, from US images, the anesthesia of 100 patients with scapula fracture who underwent regional nerve block. enhance a variety of anesthesiologists clinical abilities and responsibilities. ...
... The program is able to provide assistance to needle insertion point identification in obese patients. Liu 2021 [20] Locate, from US images, the anesthesia point of patients with regional nerve block. ...
Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.
A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.
A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.
AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals’ skills of decision-making, diagnostic accuracy, and therapeutic response.
... Another AI model improved the accuracy of the image and reduced the time from needle puncture to completion of injection (7.5 minutes compared to 10.2 minutes in the control group) in 100 patients with a scapular fracture to perform nerve block 57 . ...
Artificial intelligence (AI) is rapidly evolving and gaining attention in the medical world. Our aim is to provide readers with insights into this quickly changing medical landscape and the role of clinicians in the middle of this popular technology. In this review, our aim is to explain some of the increasingly frequently used AI terminology explicitly for physicians. Next, we give a summation, an overview of currently existing applications, future possibilities for AI in the medical field of anesthesiology and thoroughly highlight possible problems that could arise from implementing this technology in daily practice. Therefore, we conducted a literature search, including all types of articles published between the first of January 2010 and the 1st of May 2023, written in English, and having a free full text available. We searched Pubmed, Medline, and Embase using “artificial intelligence”, “machine learning”, “deep learning”, “neural networks” and “anesthesiology” as MESH terms. To structure these findings, we divided the results into five categories: preoperatively, perioperatively, postoperatively, AI in the intensive care unit and finally, AI used for teaching purposes. In the first category, we found AI applications for airway assessment, risk prediction, and logistic support. Secondly, we made a summation of AI applications used during the operation. AI can predict hypotensive events, delivering automated anesthesia, reducing false alarms, and aiding in the analysis of ultrasound anatomy in locoregional anesthesia and echocardiography. Thirdly, namely postoperatively, AI can be applied in predicting acute kidney injury, pulmonary complications, postoperative cognitive dysfunction and can help to diagnose postoperative pain in children. At the intensive care unit, AI tools discriminate acute respiratory distress syndrome (ARDS) from pulmonary oedema in pleural ultrasound, predict mortality and sepsis more accurately, and predict survival rates in severe Coronavirus-19 (COVID-19). Finally, AI has been described in training residents in spinal ultrasound, simulation, and plexus block anatomy. Several concerns must be addressed regarding the use of AI. Firstly, this software does not explain its decision process (i.e., the ‘black box problem’). Secondly, to develop AI models and decision support systems, we need big and accurate datasets, unfortunately with potential unknown bias. Thirdly, we need an ethical and legal framework before implementing this technology. At the end of this paper, we discuss whether this technology will be able to replace the clinician one day. This paper adds value to already existing literature because it not only offers a summation of existing literature on AI applications in anesthesiology but also gives clear definitions of AI itself and critically assesses implementation of this technology.
... Smistad et al. investigated the potential use of a deep convolutional neural network for ultrasound-guided axillary nerve block procedures and demonstrated that their system could identify the musculocutaneous, median, ulnar, and radial nerves as well as blood vessels in ultrasound images [24]. Liu et al. showed that a convolutional neural network improved the accuracy of ultrasound images and shortened the time required for the administration of regional anesthesia in patients with a scapular fracture [25]. Gungor et al. reported that an AI-based system helped inexperienced anesthesiologists to interpret anatomical structures in real-time during ultrasound-guided interscalene, supraclavicular, infraclavicular and transversus abdominis plane blocks [26]. ...
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.
... -Studying nerve structure and ultrasound image tracking (9); -Assessing deep-learning performance for nerve tracking in ultrasound images (10); -Studying the accuracy of real-time (AI) -based anatomical identification (11); -Assessment of CNN-based framework for needle detection in curvilinear 2D US (12); -Evaluation of success rate of spinal anesthesia of AI-assisted methods (13); -Using AI for precise needle target localization (14); -Identification of musculocutaneous, median, ulnar, and radial nerve) and blood vessels (15); -Assessment of the utility of ScanNav to identify structures, teaching and learning UGRA, and increase operator confidence (16); -Assessment of UGRA expert perception of risks of the use of ScanNav (risk of block failure, unwanted needle trauma (eg, arteries, nerves, and pleura/peritoneum (16); -Identification of the difference in accuracy between deep learning (DL)-powered ultrasound guidance and regular ultrasound images; the use of artificial intelligence to optimize regional anesthesia puncture path; to identify the effectiveness of ultrasound-guided imaging "scapular nerve block" surgical pain of the fracture (17). ...
... machine learning; PPV, positive predictive value; NPV, Negative predictive value; AUC-area under the curve; FP-false-positive; FN-false-negative.more efficient, significantly shortened the time of performing nerve block, and reduced complication rate compared to the traditional method(17). ...
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.
... With the help of ultrasound guidance, nerve block can accurately locate the anesthesia site and clarify the paths of drug diffusion, which may end up with optimal anesthesia effect and decrease in unnecessary injury. In recent years, ultrasound-guided nerve block is increasingly used in fracture surgery, especially in elderly patients [9][10][11]. However, as described in previous studies, nerve block might lead to block failure or incomplete block. ...
Objective:
Surgical reduction is the leading approach to patients with lower extremity fractures. The options of anesthetic drugs during surgery are of great significance to postoperative recovery of patients. There is no consensus on the optimum anesthesia method for patients undergoing lower extremity fracture surgery. Our study is aimed at investigating the impacts of nerve block combined with general anesthesia on perioperative outcomes of the patients.
Methods:
In this retrospective study, 48 patients experienced general anesthesia only, and 42 patients received never block combined with general anesthesia. The perioperative hemodynamics was recorded, including mean arterial pressure (MAP), oxygen saturation of blood (SpO2), and heart rate (HR). Visual analogue scale (VAS) and Montreal Cognitive Assessment (MoCA) were carried out to evaluate postoperative pain and cognitive status. Furthermore, adverse reactions and recovery condition were observed between the patients receiving different anesthesia methods.
Results:
At 15 minutes and 30 minutes after anesthesia, as well as 5 minutes after surgery, significant lower MAP was observed in the patients treated with general anesthesia (83.04 ± 8.661, 79.17 ± 9.427, 86.58 ± 8.913) compared to those receiving never block combined with general anesthesia (90.43 ± 4.618, 88.74 ± 6.224, 92.21 ± 4.015) (P < 0.05), and compared with general anesthesia group (68.5 ± 7.05, 69.63 ± 7.956, 72.75 ± 8.446), the combined anesthesia group (73.52 ± 9.451, 74.17 ± 10.13, 77.62 ± 9.768) showed obvious higher HR (P < 0.05). No significant difference in SpO2 was found between the two groups at multiple time points (P > 0.05). As for the score of VAS and MoCA, remarkably lower VAS and higher MoCA at 6 h, 12 h and 24 h after surgery were presented in the combined anesthesia group compared to general anesthesia group (P < 0.05). At 24 h after surgery, the two groups showed normal cognitive function (26.33 ± 0.7244 vs. 28.55 ± 0.7392). Incidence of nausea and vomiting in the combined anesthesia group was lower than that of the general anesthesia group (P < 0.05). The time to out-of-bed activity and hospital stay were shorter in the combined anesthesia group compared with general anesthesia (P < 0.05).
Conclusion:
The application of never block combined with general anesthesia contributed to the stability of hemodynamics, alleviation of postoperative pain and cognitive impairment, along with decrease in adverse reactions and hospital stay in the patients with lower extremity fractures.
... Meta-learning models, such as random forest, extreme gradient boosting (XGBoost), and deep learning models, especially the convolutional neural network (CNN) model and deep neural network (DNN), are trained to predict hypotension occurring between tracheal intubation and incision [18]. Deep learning and machine learning methods are proposed to help anesthesiologists make decisions about anesthesia, such as inference of brain states under anesthesia [19] and ultrasound image guidance [20]. Various indicators of postoperative anesthesia help anesthesiologists better monitor patients' vital signs after surgery, ensure smooth and safe recovery of patient consciousness during the awakening period, and strive to reduce complications during the awakening period [21]. ...
... This paper seeks an effective method to help anesthesiologists estimate the recovery time from anesthesia for each patient. Second, few studies are devoted to analyzing the importance of each feature during or after surgery [19,20]. After the analysis, anesthesiologists can quickly make a decision based on important features to estimate how long a patient will recover from anesthesia without resorting to complex machine learning or deep learning models. ...
It is significant for anesthesiologists to have a precise grasp of the recovery time of the patient after anesthesia. Accurate prediction of anesthesia recovery time can support anesthesiologist decision-making during surgery to help reduce the risk of surgery in patients. However, effective models are not proposed to solve this problem for anesthesiologists. In this paper, we seek to find effective forecasting methods. First, we collect 1824 patient anesthesia data from the eye center and then performed data preprocessing. We extracted 85 variables to predict recovery time from anesthesia. Second, we extract anesthesia information between variables for prediction using machine learning methods, including Bayesian ridge, lightGBM, random forest, support vector regression, and extreme gradient boosting. We also design simple deep learning models as prediction models, including linear residual neural networks and jumping knowledge linear neural networks. Lastly, we perform a comparative experiment of the above methods on the dataset. The experiment demonstrates that the machine learning method performs better than the deep learning model mentioned above on a small number of samples. We find random forest and XGBoost are more efficient than other methods to extract information between variables on postoperative anesthesia recovery time.
... Guided by ultrasound in local block, the doctors can clearly see the nerve structure and the blood vessels, muscles, bones, and visceral structures around the nerve. In the process of needle insertion, real-time images of needle movement can be provided to better approach the target structure [10][11][12], which is conducive to the smooth implementation of surgery. ...
This study was aimed to explore the anesthesia, analgesia, and nursing intervention scheme for elderly patients undergoing the operation of intertrochanteric fracture of femur under the guidance of ultrasound optimized by blind deblurring algorithm. Fifty elderly patients undergoing intertrochanteric femoral surgery were randomly enrolled into control group (tracheal intubation intravenous anesthesia + routine nursing) and experimental group (ultrasound-guided nerve block anesthesia + comprehensive nursing based on blind deblurring algorithm), with 25 patients in each group. The effects of anesthesia and recovery were evaluated in the two groups. The results showed that the image evaluation index of blind deblurring algorithm was superior to other algorithms (BM3D, DnCNN, and Red-Net), which improved the quality of ultrasound imaging and was more conducive to intraoperative anesthesia guidance. At the beginning and end of intubation and operation, the fluctuation range of mean arterial pressure (MAP) and heart rate (HR) in the experimental group was lower than that in the control group. The maintenance time of sensory and motor anesthesia block (7.53 ± 1.47 h, 5.45 ± 1.36 h) was longer than that of control group (3.38 ± 1.26 h, 3.02 ± 1.31 h). Visual Analogue Scale/Score (VAS) scores at 6 h, 12 h, and 24 h after surgery were lower than those in the control group. The effective rate of nursing and the incidence of complications (92% and 8%) were better than the control group (80% and 16%), and the difference was statistically significant (P<0.05). In summary, the optimization effect of blind deblurring algorithm was good, which can improve the quality of ultrasound-guided surgery and help in the smooth implementation of surgery. Moreover, nerve block anesthesia and comprehensive nursing were of great value in postoperative analgesia and recovery of patients.
: Anesthesia is the process of inducing and experiencing various conditions, such as painlessness, immobility, and amnesia, to facilitate surgeries and other medical procedures. During the administration of anesthesia, anesthesiologists face critical decision-making moments, considering the significance of the procedure and potential complications resulting from anesthesia-related choices. In recent years, artificial intelligence (AI) has emerged as a supportive tool for anesthesia decisions, given its potential to assist with control and management tasks. This study aims to conduct a comprehensive review of articles on the intersection of AI and anesthesia. A review was conducted by searching PubMed for peer-reviewed articles published between 2020 and early 2022, using keywords related to anesthesia and AI. The articles were categorized into nine distinct groups: "Depth of anesthesia", "Control of anesthesia delivery", "Control of mechanical ventilation and weaning", "Event prediction", "Ultrasound guidance", "Pain management", "Operating room logistic", "Monitoring", and "Neuro-critical care". Four reviewers meticulously examined the selected articles to extract relevant information. The studies within each category were reviewed by considering items such as the purpose and type of anesthesia, AI algorithms, dataset, data accessibility, and evaluation criteria. To enhance clarity, each category was analyzed with a higher resolution than previous review articles, providing readers with key points, limitations, and potential areas for future research to facilitate a better understanding of each concept. The advancements in AI techniques hold promise in significantly enhancing anesthesia practices and improving the overall experience for anesthesiologists.
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.
