Automatic Segmentation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intelligence.
Ultrasound-guided quadratus lumborum block (QLB) technology has become a widely used perioperative analgesia method during abdominal and pelvic surgeries. Due to the anatomical complexity and individual variability of the quadratus lumborum muscle (QLM) on ultrasound images, nerve blocks heavily rel...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1362 - 1374 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280512&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280512 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280512 185280512 189894326 185280512 10.1007/s10278-024-01267-8 185280512 ppf: 1362 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Segmentation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intelligence. aug: au: Wang, Qiang He, Bingxi Yu, Jie Zhang, Bowen Yang, Jingchao Liu, Jin Ma, Xinwei Wei, Shijing Li, Shuai Zheng, Hui Tang, Zhenchao affil: https://ror.org/02drdmm93 Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, 100021, Beijing, China sug: subj: Nerve Block Quadratus Lumborum Muscles Innervation Quadratus Lumborum Muscles Ultrasonography Quadratus Lumborum Muscles Anatomy and Histology Image Processing, Computer Assisted Image Interpretation, Computer Assisted Detection Algorithms Automation Human Male Female Adolescence Adult Middle Age Aged China Academic Medical Centers Funding Source Retrospective Design Record Review Prospective Studies Descriptive Statistics Predictive Value of Tests False Positive Results False Negative Results Analysis of Variance Deep Learning Convolutional Neural Networks Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Ultrasound-guided quadratus lumborum block (QLB) technology has become a widely used perioperative analgesia method during abdominal and pelvic surgeries. Due to the anatomical complexity and individual variability of the quadratus lumborum muscle (QLM) on ultrasound images, nerve blocks heavily rely on anesthesiologist experience. Therefore, using artificial intelligence (AI) to identify different tissue regions in ultrasound images is crucial. In our study, we retrospectively collected 112 patients (3162 images) and developed a deep learning model named Q-VUM, which is a U-shaped network based on the Visual Geometry Group 16 (VGG16) network. Q-VUM precisely segments various tissues, including the QLM, the external oblique muscle, the internal oblique muscle, the transversus abdominis muscle (collectively referred to as the EIT), and the bones. Furthermore, we evaluated Q-VUM. Our model demonstrated robust performance, achieving mean intersection over union (mIoU), mean pixel accuracy, dice coefficient, and accuracy values of 0.734, 0.829, 0.841, and 0.944, respectively. The IoU, recall, precision, and dice coefficient achieved for the QLM were 0.711, 0.813, 0.850, and 0.831, respectively. Additionally, the Q-VUM predictions showed that 85% of the pixels in the blocked area fell within the actual blocked area. Finally, our model exhibited stronger segmentation performance than did the common deep learning segmentation networks (0.734 vs. 0.720 and 0.720, respectively). In summary, we proposed a model named Q-VUM that can accurately identify the anatomical structure of the quadratus lumborum in real time. This model aids anesthesiologists in precisely locating the nerve block site, thereby reducing potential complications and enhancing the effectiveness of nerve block procedures. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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