1
|
Bhushan S, Liu X, Jiang F, Wang X, Mao L, Xiao Z. A progress of research on the application of fascial plane blocks in surgeries and their future direction: a review article. Int J Surg 2024; 110:3633-3640. [PMID: 38935829 PMCID: PMC11175748 DOI: 10.1097/js9.0000000000001282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 02/22/2024] [Indexed: 06/29/2024]
Abstract
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.
Collapse
Affiliation(s)
- Sandeep Bhushan
- Department of Cardio-Thoracic Surgery, Chengdu Second People’s Hospital
| | - Xian Liu
- Department of Cardio-Thoracic Surgery, Chengdu Second People’s Hospital
| | - Fenglin Jiang
- Department of Anesthesia and Surgery, Chengdu Second People’s Hospital, Chengdu, Sichuan, People’s Republic of China
| | - Xiaowei Wang
- Department of Cardio-Thoracic Surgery, Chengdu Second People’s Hospital
| | - Long Mao
- Department of Cardio-Thoracic Surgery, Chengdu Second People’s Hospital
| | - Zongwei Xiao
- Department of Cardio-Thoracic Surgery, Chengdu Second People’s Hospital
| |
Collapse
|
2
|
Bowness JS, Metcalfe D, El-Boghdadly K, Thurley N, Morecroft M, Hartley T, Krawczyk J, Noble JA, Higham H. Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines. Br J Anaesth 2024; 132:1049-1062. [PMID: 38448269 PMCID: PMC11103083 DOI: 10.1016/j.bja.2024.01.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/09/2024] [Accepted: 01/24/2024] [Indexed: 03/08/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- James S Bowness
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
| | - David Metcalfe
- Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK; Emergency Medicine Research in Oxford (EMROx), Oxford University Hospitals NHS Foundation Trust, Oxford, UK. https://twitter.com/@TraumaDataDoc
| | - Kariem El-Boghdadly
- Department of Anaesthesia and Peri-operative Medicine, Guy's & St Thomas's NHS Foundation Trust, London, UK; Centre for Human and Applied Physiological Sciences, King's College London, London, UK. https://twitter.com/@elboghdadly
| | - Neal Thurley
- Bodleian Health Care Libraries, University of Oxford, Oxford, UK
| | - Megan Morecroft
- Faculty of Medicine, Health & Life Sciences, University of Swansea, Swansea, UK
| | - Thomas Hartley
- Intelligent Ultrasound, Cardiff, UK. https://twitter.com/@tomhartley84
| | - Joanna Krawczyk
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK
| | - J Alison Noble
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK. https://twitter.com/@AlisonNoble_OU
| | - Helen Higham
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. https://twitter.com/@HelenEHigham
| |
Collapse
|
3
|
Xi Y, Chong H, Zhou Y, Zhu F, Yao Y, Wang G. Convolutional neural network for brachial plexus segmentation at the interscalene level. BMC Anesthesiol 2024; 24:17. [PMID: 38191333 PMCID: PMC10773123 DOI: 10.1186/s12871-024-02402-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 01/03/2024] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Regional anesthesia with ultrasound-guided brachial plexus block is widely used for patients undergoing shoulder and upper limb surgery, but needle misplacement can result in complications. The purpose of this study was to develop and validate a convolutional neural network (CNN) model for segmentation of the brachial plexus at the interscalene level. METHODS This prospective study included patients who underwent ultrasound-guided brachial plexus block in the Anesthesiology Department of Beijing Jishuitan Hospital between October 2019 and June 2022. A Unet semantic segmentation model was developed to train the CNN to identify the brachial plexus features in the ultrasound images. The degree of overlap between the predicted segmentation and ground truth segmentation (manually drawn by experienced clinicians) was evaluated by calculation of the Dice index and Jaccard index. RESULTS The final analysis included 502 images from 127 patients aged 41 ± 14 years-old (72 men, 56.7%). The mean Dice index was 0.748 ± 0.190, which was extremely close to the threshold level of 0.75 for good overlap between the predicted and ground truth segregations. The Jaccard index was 0.630 ± 0.213, which exceeded the threshold value of 0.5 for a good overlap. CONCLUSION The CNN performed well at segregating the brachial plexus at the interscalene level. Further development could allow the CNN to be used to facilitate real-time identification of the brachial plexus during interscalene block administration. CLINICAL TRIAL REGISTRATION The trial was registered prior to patient enrollment at the Chinese Clinical Trial Registry (ChiCTR2200055591), the site url is https://www.chictr.org.cn/ . The date of trial registration and patient enrollment is 14/01/2022.
Collapse
Affiliation(s)
- Yang Xi
- Department of Pain Managemengt, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| | - Hao Chong
- Department of Pain Managemengt, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| | - Yan Zhou
- Department of Pain Managemengt, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| | - Feng Zhu
- Department of Anesthesiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| | - Yuhang Yao
- Beijing AMIT Medical Science and Technology Ltd., Co, Beijing, 100000, China
| | - Geng Wang
- Department of Anesthesiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China.
| |
Collapse
|
4
|
Zhao Y, Zheng S, Cai N, Zhang Q, Zhong H, Zhou Y, Zhang B, Wang G. Utility of Artificial Intelligence for Real-Time Anatomical Landmark Identification in Ultrasound-Guided Thoracic Paravertebral Block. J Digit Imaging 2023; 36:2051-2059. [PMID: 37291383 PMCID: PMC10501964 DOI: 10.1007/s10278-023-00851-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 06/10/2023] Open
Abstract
Thoracic paravertebral block (TPVB) is a common method of inducing perioperative analgesia in thoracic and abdominal surgery. Identifying anatomical structures in ultrasound images is very important especially for inexperienced anesthesiologists who are unfamiliar with the anatomy. Therefore, our aim was to develop an artificial neural network (ANN) to automatically identify (in real-time) anatomical structures in ultrasound images of TPVB. This study is a retrospective study using ultrasound scans (both video and standard still images) that we acquired. We marked the contours of the paravertebral space (PVS), lung, and bone in the TPVB ultrasound image. Based on the labeled ultrasound images, we used the U-net framework to train and create an ANN that enabled real-time identification of important anatomical structures in ultrasound images. A total of 742 ultrasound images were acquired and labeled in this study. In this ANN, the Intersection over Union (IoU) and Dice similarity coefficient (DSC or Dice coefficient) of the paravertebral space (PVS) were 0.75 and 0.86, respectively, the IoU and DSC of the lung were 0.85 and 0.92, respectively, and the IoU and DSC of the bone were 0.69 and 0.83, respectively. The accuracies of the PVS, lung, and bone were 91.7%, 95.4%, and 74.3%, respectively. For tenfold cross validation, the median interquartile range for PVS IoU and DSC was 0.773 and 0.87, respectively. There was no significant difference in the scores for the PVS, lung, and bone between the two anesthesiologists. We developed an ANN for the real-time automatic identification of thoracic paravertebral anatomy. The performance of the ANN was highly satisfactory. We conclude that AI has good prospects for use in TPVB. Clinical registration number: ChiCTR2200058470 (URL: http://www.chictr.org.cn/showproj.aspx?proj=152839 ; registration date: 2022-04-09).
Collapse
Affiliation(s)
- Yaoping Zhao
- Department of Anesthesiology, Beijing Jishuitan Hospital, No. 31 of Xinjiekou East Street, Xicheng District, Beijing, 100035, China
| | - Shaoqiang Zheng
- Department of Anesthesiology, Beijing Jishuitan Hospital, No. 31 of Xinjiekou East Street, Xicheng District, Beijing, 100035, China
| | - Nan Cai
- Department of Anesthesiology, Beijing Jishuitan Hospital, No. 31 of Xinjiekou East Street, Xicheng District, Beijing, 100035, China
| | - Qiang Zhang
- Department of Thoracic Surgery, Beijing Jishuitan Hospital, Beijing, 100035, China
| | - Hao Zhong
- Department of Anesthesiology, Beijing Jishuitan Hospital, No. 31 of Xinjiekou East Street, Xicheng District, Beijing, 100035, China
| | - Yan Zhou
- Department of Anesthesiology, Beijing Jishuitan Hospital, No. 31 of Xinjiekou East Street, Xicheng District, Beijing, 100035, China
| | - Bo Zhang
- AMIT Co., Ltd., Wuxi , Jiangsu, 214000, China
| | - Geng Wang
- Department of Anesthesiology, Beijing Jishuitan Hospital, No. 31 of Xinjiekou East Street, Xicheng District, Beijing, 100035, China.
| |
Collapse
|
5
|
Berggreen J, Johansson A, Jahr J, Möller S, Jansson T. Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision. Healthcare (Basel) 2023; 11:healthcare11020184. [PMID: 36673552 PMCID: PMC9859453 DOI: 10.3390/healthcare11020184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/30/2022] [Accepted: 01/05/2023] [Indexed: 01/10/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Johan Berggreen
- Biomedical Engineering, Department of Clinical Sciences Lund, Lund University, Lasarettsgatan 37, 22185 Lund, Sweden
- Intensive and Perioperative Care, Skåne University Hospital, Entregatan 7, 22185 Lund, Sweden
| | - Anders Johansson
- Biomedical Engineering, Department of Clinical Sciences Lund, Lund University, Lasarettsgatan 37, 22185 Lund, Sweden
| | - John Jahr
- Biomedical Engineering, Department of Clinical Sciences Lund, Lund University, Lasarettsgatan 37, 22185 Lund, Sweden
| | - Sebastian Möller
- Biomedical Engineering, Department of Clinical Sciences Lund, Lund University, Lasarettsgatan 37, 22185 Lund, Sweden
- Department of Information Technology and Clinical Engineering, Skåne Regional Council, Lasarettsgatan 37, 22185 Lund, Sweden
| | - Tomas Jansson
- Biomedical Engineering, Department of Clinical Sciences Lund, Lund University, Lasarettsgatan 37, 22185 Lund, Sweden
- Department of Information Technology and Clinical Engineering, Skåne Regional Council, Lasarettsgatan 37, 22185 Lund, Sweden
- Correspondence:
| |
Collapse
|
6
|
Viderman D, Dossov M, Seitenov S, Lee MH. Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review. Front Med (Lausanne) 2022; 9:994805. [PMID: 36388935 PMCID: PMC9640918 DOI: 10.3389/fmed.2022.994805] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/22/2022] [Indexed: 01/06/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Dmitriy Viderman
- Department of Biomedical Sciences, Nazarbayev University School of Medicine, Nur-Sultan, Kazakhstan
| | - Mukhit Dossov
- Department of Anesthesiology and Critical Care, Presidential Hospital, Nur-Sultan, Kazakhstan
| | - Serik Seitenov
- Department of Anesthesiology and Critical Care, Presidential Hospital, Nur-Sultan, Kazakhstan
| | - Min-Ho Lee
- Department of Computer Sciences, Nazarbayev University School of Engineering and Digital Sciences, Nur-Sultan, Kazakhstan
| |
Collapse
|
7
|
Vanhonacker D, Verdonck M, Nogueira Carvalho H. Impact of Closed-Loop Technology, Machine Learning, and Artificial Intelligence on Patient Safety and the Future of Anesthesia. CURRENT ANESTHESIOLOGY REPORTS 2022. [DOI: 10.1007/s40140-022-00539-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|