Using positional tracking to improve abdominal ultrasound machine learning classification
Lawley, Alistair and Hampson, Rory and Worrall, Kevin and Dobie, Gordon (2024) Using positional tracking to improve abdominal ultrasound machine learning classification. Machine Learning: Science and Technology, 5 (2). 025002. ISSN 2632-2153 (https://doi.org/10.1088/2632-2153/ad379d)
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Abstract
Diagnostic 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 to sonographers 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.
ORCID iDs
Lawley, Alistair ORCID: https://orcid.org/0000-0002-0903-1116, Hampson, Rory ORCID: https://orcid.org/0000-0001-7903-7460, Worrall, Kevin and Dobie, Gordon ORCID: https://orcid.org/0000-0003-3972-5917;-
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Item type: Article ID code: 88696 Dates: DateEvent30 June 2024Published2 April 2024Published Online25 March 2024Accepted14 September 2023SubmittedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 15 Apr 2024 06:56 Last modified: 20 Dec 2024 02:12 URI: https://strathprints.strath.ac.uk/id/eprint/88696