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While ultrasonography improves success and reduces complications, accurate identification of nerves or vertebrae can be disrupted by anatomical variation, noise, and positional instability. Machine learning offers predictive, programming-free model approaches, with studies reporting improved tracking accuracy and real-time image localization, alongside time savings.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/precision-in-practice-exploring-the-impact-of-ai-and-machine-learning-on-ultrasound-guided-regional-anaesthesia/128084/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/precision-in-practice-exploring-the-impact-of-ai-and-machine-learning-on-ultrasound-guided-regional-anaesthesia/128084.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"UGRA为何在手术和镇痛中被广泛使用？","Question",{"text":113,"@type":114},"UGRA用于择期与急诊手术及疼痛管理，优势包括非侵入、具成本效益、易获取，并能为麻醉医师提供解剖标志、针尖进展及局部麻醉药扩散的清晰可视化。","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"影响UGRA中关键解剖结构识别的主要因素有哪些？",{"text":118,"@type":114},"关键解剖结构（如神经或椎体）的识别可能受到神经结构与位置差异、噪声干扰以及体位不稳定的影响。",{"name":120,"@type":111,"acceptedAnswer":121},"AI/机器学习在超声引导区域麻醉中带来哪些改进证据？",{"text":122,"@type":114},"相关研究使用CNN等模型实现较高精度的神经追踪与图像定位，并通过SVM与模板匹配等方法在实时条件下确定腰段水平及穿刺位点；多项研究同时报告了显著节省时间的效果。","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},128084,1785944717,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":81},5909887254083,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","1394  \nLETTER TO THE EDITOR  \nPrecision in practice: Exploring the impact of AI and machine learning on ultrasound guided regional anaesthesia  \nNoor Ul Huda Bhatti1, Syed Ghazi Ali Kirmani2, Maryam Butt3  \nDear Madam, Ultrasound Guided Regional Anaesthesia (UGRA) is a commonly utilized practice in both elective and emergency situations during surgical procedures and for pain management. Its benefits include, being non-invasive, cost-effective, readily accessible, and providing the anaesthetist with clear visualization of essential anatomical landmarks, needle progression, and the spread of local anaesthetic. Ultrasonography has been shown to increase success rates for regional anaesthesia and decrease complications. One of the critical steps during UGRA is identifying relevant anatomical structures like nerves or vertebras. However, this aspect can be hindered by external influences such as variations in nerve structure and position, interference from noise, and positional instability.  \nMachine learning is a promising branch of artificial intelligence. It is used to conduct predictive tasks without programming instructions by creating algorithms. Extensive research has been conducted to evaluate the influence of machine learning on innovative anaesthesia methods. In 2023, Lopez et al. published a systematic review on how Artificial Intelligence could positively impact traditional anaesthesia practices.1 Various studies included in the review employed different models to achieve variable targets during the induction of anaesthesia.  \nIn one experiment, Alkhatib et al. used Convolutional neural network (CNN) based deep trackers to track the median and sciatic nerve with a surprising accuracy of 0.87.2 Another study employed the same CNN model to  \n1Department of Surgical Oncology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan; 2Allama Iqbal Medical College, Lahore, Pakistan; 3King Edward Medical University, Lahore, Pakistan. Correspondence: Noor Ul Huda Bhatti. e-mail: [nooorulhudaaa219@gmail.com](nooorulhudaaa219@gmail.com)   \nORCID ID. 0009-0005-0617-9800 Submission complete: 25-04-2024  \nAcceptance: 09-05-2024  \nReview began: 28-04-2024  \nReview end: 05-05-2024  \nlocate and discriminate accurate images of sacrum, vertebral levels and intervertebral gaps during percutaneous spinal needle insertion.3 Another study used a different AI model called SVM (support vector machine) classification, image processing, and template matching to locate lumbar level L3-L4 and the ideal puncture site forepidural anaesthesia in real-time. In this experiment, anaesthetists with minimal experience in ultrasonography were able to successfully determine needle puncture sites accurately.4 All these studies utilizing AI models not only yielded beneficial results but also led to significant time savings.  \nConsidering this, anaesthesio logists nationwide must integrate effective AI models to improve their clinical practice. This would minimize adverse outcomes in regional anaesthesia and ultimately enhance patient care and satisfaction.  \nDisclaimer: None.  \nConflict of Interest: None.  \nFunding Source: None.  \nDOI: [https://doi.org/10.47391/JPMA.20467](https://doi.org/10.47391/JPMA.20467)  \nReferences  \n1. Lopes S, Rocha G, Guimarães-Pereira L. Artificial intelligence and its clinical application in Anesthesiology: a systematic review. J Clin Monit Comput. 2024; 38:247-59.doi: 10. 1007/s 10877-023-01088-0.  \n2. Alkhatib M, Hafiane A, Vieyres P, Delbos A. Deep visual nerve tracking in ultrasound images. Comput Med Imaging Graph. 2019; 76: 101639. doi: 10. 1016/j.compmedimag.2019.05.007.  \n3. Hetherington J, Lessoway V, Gunka V, Abolmaesumi P, Rohling R. SLIDE: automatic spine level identification system using a deep convolutional neural network. Int J Comput Assist Radiol Surg. 2017;  \n12: 1189-98. doi: 10. 1007/s 11548-017-1575-8.  \n4. LengY, Yu S, Tan KK, Tildsley P, Sia AT, Sng BL. Development of a realtime lumbar ult","cbCairQvydjLRgOI","https://ap.wps.com/l/cbCairQvydjLRgOI","pdf",45335,"English","# Letter to the Editor\n## Overview of UGRA and clinical challenges\n## Role of machine learning and evidence from studies\n## Call for integrating AI models in practice\n## Declarations and reference information","[{\"question\":\"UGRA为何在手术和镇痛中被广泛使用？\",\"answer\":\"UGRA用于择期与急诊手术及疼痛管理，优势包括非侵入、具成本效益、易获取，并能为麻醉医师提供解剖标志、针尖进展及局部麻醉药扩散的清晰可视化。\"},{\"question\":\"影响UGRA中关键解剖结构识别的主要因素有哪些？\",\"answer\":\"关键解剖结构（如神经或椎体）的识别可能受到神经结构与位置差异、噪声干扰以及体位不稳定的影响。\"},{\"question\":\"AI/机器学习在超声引导区域麻醉中带来哪些改进证据？\",\"answer\":\"相关研究使用CNN等模型实现较高精度的神经追踪与图像定位，并通过SVM与模板匹配等方法在实时条件下确定腰段水平及穿刺位点；多项研究同时报告了显著节省时间的效果。\"}]","Precision in practice - Exploring the impact of AI and machine learning on ultrasound guided regional anaesthesia | PDF"]