[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121872-en":3,"doc-seo-121872-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},121872,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Using positional tracking to improve abdominal ultrasound machine learning classification","Diagnostic abdominal ultrasound screening depends on capturing standardized cross-sectional images that cover relevant anatomical structures for reliable clinical decisions. Conventional image-only machine learning offers limited support because visually similar regions—such as multiple liver cross sections or left/right kidney views—can be difficult to distinguish after acquisition. This proof of concept adds positional tracking context by using optical and infrared sensor-based tracking of an ultrasound probe during data collection, then training convolutional neural networks for comparison.","Machine Learning: Science and  \nTechnology   \nPAPER • OPEN ACCESS  \nUsing positional tracking to improve abdominal ultrasound machine learning classification  \nTo cite this article: Alistair Lawley et al 2024 Mach. Learn. : Sci. Technol. 5 025002  \nView the article online for updates and enhancements.  \nYou may also like  \n-Paramagnetic perfluorocarbon-filled albumin-(Gd-DTPA) microbubbles for the induction of focused-ultrasound-induced blood–brain barrier opening and concurrent MR and ultrasound imaging  \nAi-Ho Liao, Hao-Li Liu, Chia-Hao Su et al.  \n-INVESTIGATING THE CORE MORPHOLOGY–SEYFERT CLASS RELATIONSHIP WITH HUBBLE SPACE TELESCOPE ARCHIVAL IMAGES OF LOCAL SEYFERT GALAXIES  \nM. J. Rutkowski, P. R. Hegel, Hwihyun Kim et al.  \n-Simulations of the WFIRST Supernova Survey and Forecasts of Cosmological Constraints  \nR. Hounsell, D. Scolnic, R. J. Foley et al.  \nThis content was downloaded from IP address [2.216.52.39](2.216.52.39) on 02/07/2024 at 15:20  \n 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 adh","cbCairTBForyqEcr","https://ap.wps.com/l/cbCairTBForyqEcr","pdf",1682068,1,13,"English","en",105,"# Introduction\n## Problem and motivation\n## Prior work and limitations\n## Positional tracking approach\n## Experimental setup and model training\n## Results and comparison\n## Discussion and future potential","[{\"question\":\"What limitation of image-only machine learning exists for abdominal ultrasound classification?\",\"answer\":\"Visually similar or closely related cross-sectional images can be difficult to differentiate when classification relies on image content alone, leading to limited assistance for protocol adherence.\"},{\"question\":\"How does positional tracking improve the ultrasound classification task?\",\"answer\":\"Positional tracking provides additional context about the ultrasound probe location, helping the model recognize six difficult edge cases that are otherwise hard to identify from image alone.\"},{\"question\":\"What tracking methods were evaluated in the proof of concept?\",\"answer\":\"Optical infrared tracking and sensor-based infrared tracking were used to track probe position during clinical cross-section collection on an abdominal phantom, and their classification performance was compared.\"}]","Using positional tracking to improve abdominal ultrasound machine learning classification | 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limitation of image-only machine learning exists for abdominal ultrasound classification?","Question",{"text":75,"@type":76},"Visually similar or closely related cross-sectional images can be difficult to differentiate when classification relies on image content alone, leading to limited assistance for protocol adherence.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does positional tracking improve the ultrasound classification task?",{"text":80,"@type":76},"Positional tracking provides additional context about the ultrasound probe location, helping the model recognize six difficult edge cases that are otherwise hard to identify from image alone.",{"name":82,"@type":73,"acceptedAnswer":83},"What tracking methods were evaluated in the proof of concept?",{"text":84,"@type":76},"Optical infrared tracking and sensor-based infrared tracking were used to track probe position during clinical cross-section collection on an abdominal phantom, and their classification performance 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