[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121822-en":3,"doc-seo-121822-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121822,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",7,"Healthcare","Using positional tracking to improve abdominal ultrasound machine learning classification","Diagnostic abdominal ultrasound relies on standardized cross-sectional image collection to ensure coverage of key anatomy for reliable clinical decisions. Image-only machine learning offers limited support for protocol adherence, especially when differentiating visually similar liver and kidney cross sections after acquisition. This proof-of-concept adds positional tracking to a neural network to supply contextual information for six challenging edge cases. Optical and low-cost infrared tracking systems were used to measure probe position during clinical cross-section capture on an abdominal phantom, improving accuracy from ~90% to 95% (optical IR) and 93% (sensor-based IR).","Mach. Learn.: Sci. Technol. 5 (2024) 025002 [https://doi.org/10.1088/2632-2153/ad379d](https://doi.org/10.1088/2632-2153/ad379d)  \nOPEN ACCESS  \nRECEIVED  \n14 September 2023  \nREVISED  \n1 February 2024  \nACCEPTED FOR PUBLICATION 25 March 2024  \nPUBLISHED  \n2 April 2024  \nOriginal content from this work may be used under the terms of the  \nCreative Commons Attribution 4 .0 licence.  \nAny further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nPAPER  \nUsing positional tracking to improve abdominal ultrasound machine learning classification  \nAlistair Lawley1, ∗􀁂, Rory Hampson1, Kevin Worrall2 and Gordon Dobie1  \n1 Department of Electronic and Electrical Engineering, University of Strathclyde, Royal College building, 204 George St, Glasgow G1 1XW, United Kingdom  \n2 Department of Engineering, University of Glasgow, James Watt South Building, University of Glasgow, Glasgow G12 8QQ, United Kingdom  \n∗ Author to whom any correspondence should be addressed.  \n[E-mail: Alistair.Lawley@strath.ac.uk](E-mail: Alistair.Lawley@strath.ac.uk)  \nKeywords: machine learning, ultrasound, classification, infrared sensors  \nAbstract  \nDiagnostic abdominal ultrasound screening and monitoring protocols are based around gathering a set of standard cross sectional images that ensure the coverage of relevant anatomical structures during the collection procedure. This allows clinicians to make diagnostic decisions with the best picture available from that modality. Currently, there is very little assistance provided tosonographers to ensure adherence to collection protocols, with previous studies suggesting that traditional image only machine learning classification can provide only limited assistance in supporting this task, for example it can be difficult to differentiate between multiple liver cross sections or those of the left and right kidney from image post collection. In this proof of concept, positional tracking information was added to the image input of a neural network to provide the additional context required to recognize six otherwise difficult to identify edge cases. In this paper optical and sensor based infrared tracking (IR) was used to track the position of an ultrasound probe during the collection of clinical cross sections on an abdominal phantom. Convolutional neural networks were then trained using both image-only and image with positional data, the classification accuracy results were then compared. The addition of positional information significantly improved average classification results from ∼90% for image-only to 95% for optical IR position tracking and 93% for Sensor-based IR in common abdominal cross sections. While there is further work to be done, the addition of low-cost positional tracking to machine learning ultrasound classification will allow for significantly increased accuracy for identifying important diagnostic cross sections, with the potential to not only provide validation of adherence to protocol but also could provide navigation prompts to assist in user training and in ensuring adherence in capturing cross sections in future.  \n1. Introduction  \nDiagnostic ultrasound relies on the capture of cross-sectional images of anatomical structures within the body to provide a clinician with the requisite information to make a clinical decision. Capturing these anatomical cross sections is time consuming and requires a high level of user skill in anatomy and ultrasound operation [1, 2]. Machine learning has the potential to reduce the skill floor by assisting and automating ultrasound capture procedures, but to do so it must overcome the two fundamental difficulties: the differentiation of anatomical cross sections that are in close proximity and those that are visually similar. This is exampled in previous studies [3, 4] showing that both experienced clinicians and neural networks [5] have substantial difficulty classifying abdominal cross","cbCaihNY5HE2StQX","https://ap.wps.com/l/cbCaihNY5HE2StQX","pdf",1560448,1,12,"English","en",105,"# Introduction\n## Ultrasound cross-sectional capture and protocol challenges\n## Prior image-only machine learning classification and its limitations\n## Positional tracking approach and study design","[{\"question\":\"Why is image-only machine learning limited for abdominal ultrasound classification?\",\"answer\":\"Because abdominal cross sections in close proximity can look visually similar, making it difficult to distinguish between anatomically corresponding classes from images alone.\"},{\"question\":\"What positional tracking method was evaluated in this proof of concept?\",\"answer\":\"Optical infrared tracking using a Vicon system and a low-cost infrared tracking system based on ASIC IR sensors were used to track the ultrasound probe position.\"},{\"question\":\"How much did adding positional information improve classification accuracy?\",\"answer\":\"Average accuracy improved from about 90% for image-only classification to 95% with optical IR positional tracking and 93% with sensor-based IR in common abdominal cross sections.\"}]","Using positional tracking to improve abdominal ultrasound machine learning classification | 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is image-only machine learning limited for abdominal ultrasound classification?","Question",{"text":76,"@type":77},"Because abdominal cross sections in close proximity can look visually similar, making it difficult to distinguish between anatomically corresponding classes from images alone.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What positional tracking method was evaluated in this proof of concept?",{"text":81,"@type":77},"Optical infrared tracking using a Vicon system and a low-cost infrared tracking system based on ASIC IR sensors were used to track the ultrasound probe position.",{"name":83,"@type":74,"acceptedAnswer":84},"How much did adding positional information improve classification accuracy?",{"text":85,"@type":77},"Average accuracy improved from about 90% for image-only classification to 95% with optical IR positional tracking and 93% with sensor-based IR in common abdominal cross 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