Ultrasound Images Guided under Deep Learning in the Anesthesia Effect of the Regional Nerve Block on Scapular Fracture Surgery

Artificial intelligence and its clinical application in Anesthesiology: a systematic review

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

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.

Anesthesiologists' Perspective on the Use of Artificial Intelligence in Ultrasound-Guided Regional Anaesthesia in Terms of Medical Ethics and Medical Education: A Survey Study

OBJECTIVE

Controversy exists around the world as experts disagree on what artificial intelligence will imply for humanity in the future. Medical experts are starting to share perspectives on artificial intelligence with ethical and legal concerns appearing to prevail. The purpose of this study was to determine how anesthesiology and reanimation specialists in Turkey perceive the use of artificial intelligence in ultrasound-guided regional anesthetic applications in terms of medical ethics and education, as well as their perspectives on potential ethical issues.

MATERIALS AND METHODS

This descriptive and cross-sectional survey was conducted across Turkey between July 1 and August 31. Data were collected through an online questionnaire distributed by national associations and social media platforms. The questionnaire included questions about the descriptive features of the participants and the possible ethical problems that may be encountered in the use of artificial intelligence in regional anesthesia and 20 statements that were requested to be evaluated.

RESULTS

The average age of the 285 anesthesiologists who took part in the study was 42.00 ± 7.51, 144 of them were male, the average years spent in the field was 10.95 ± 7.15 years, 59.3% were involved in resident training, and 74.7% habitually used ultrasound guidance regional anesthetic applications. Of the participants, 80% thought artificial intelligence would benefit patients, 86.7% thought it would benefit resident training, 81.4% thought it would benefit post-graduate medical education, and 80.7% thought it would decrease complications in practice. There will be no ethical issues if sonographic data are captured anonymously, according to 78.25%, while 67% are concerned about who will be held accountable for inaccuracies.

CONCLUSION

The majority of anesthetists believe that using artificial intelligence in regional anesthetic applications will decrease complications. Although ethical concerns about privacy and data governance are low, participants do have ethical worries about "accountability for errors."

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.

Impacts of Ultrasound-Guided Nerve Block Combined with General Anesthesia with Laryngeal Mask on the Patients with Lower Extremity Fractures

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.

Quantitative Analysis of Anesthesia Recovery Time by Machine Learning Prediction 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.

Intelligent Algorithm-Based Ultrasound for Evaluating the Anesthesia and Nursing Intervention for Elderly Patients with Femoral Intertrochanteric Fractures

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.