Convolutional neural network for brachial plexus segmentation at the interscalene level
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)...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Anesthesiology |
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| Online Access: | https://doi.org/10.1186/s12871-024-02402-2 |
| _version_ | 1797355596371984384 |
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| author | Yang Xi Hao Chong Yan Zhou Feng Zhu Yuhang Yao Geng Wang |
| author_facet | Yang Xi Hao Chong Yan Zhou Feng Zhu Yuhang Yao Geng Wang |
| author_sort | Yang Xi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T14:13:24Z |
| format | Article |
| id | doaj.art-ab9b049e13f1439286d29938d3fba104 |
| institution | Directory Open Access Journal |
| issn | 1471-2253 |
| language | English |
| last_indexed | 2024-03-08T14:13:24Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Anesthesiology |
| spelling | doaj.art-ab9b049e13f1439286d29938d3fba1042024-01-14T12:34:06ZengBMCBMC Anesthesiology1471-22532024-01-012411710.1186/s12871-024-02402-2Convolutional neural network for brachial plexus segmentation at the interscalene levelYang Xi0Hao Chong1Yan Zhou2Feng Zhu3Yuhang Yao4Geng Wang5Department of Pain Managemengt, Beijing Jishuitan Hospital, Capital Medical UniversityDepartment of Pain Managemengt, Beijing Jishuitan Hospital, Capital Medical UniversityDepartment of Pain Managemengt, Beijing Jishuitan Hospital, Capital Medical UniversityDepartment of Anesthesiology, Beijing Jishuitan Hospital, Capital Medical UniversityBeijing AMIT Medical Science and Technology Ltd., CoDepartment of Anesthesiology, Beijing Jishuitan Hospital, Capital Medical UniversityAbstract 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.https://doi.org/10.1186/s12871-024-02402-2Brachial plexus blockAnesthesia regionalUltrasound imagingNeural network modelsValidation study |
| spellingShingle | Yang Xi Hao Chong Yan Zhou Feng Zhu Yuhang Yao Geng Wang Convolutional neural network for brachial plexus segmentation at the interscalene level BMC Anesthesiology Brachial plexus block Anesthesia regional Ultrasound imaging Neural network models Validation study |
| title | Convolutional neural network for brachial plexus segmentation at the interscalene level |
| title_full | Convolutional neural network for brachial plexus segmentation at the interscalene level |
| title_fullStr | Convolutional neural network for brachial plexus segmentation at the interscalene level |
| title_full_unstemmed | Convolutional neural network for brachial plexus segmentation at the interscalene level |
| title_short | Convolutional neural network for brachial plexus segmentation at the interscalene level |
| title_sort | convolutional neural network for brachial plexus segmentation at the interscalene level |
| topic | Brachial plexus block Anesthesia regional Ultrasound imaging Neural network models Validation study |
| url | https://doi.org/10.1186/s12871-024-02402-2 |
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