Skill, or Style? Classification of Fetal Sonography Eye-Tracking Data

Clare Teng, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble

Research output: Contribution to journalConference articlepeer-review

Abstract

We present a method for classifying human skill at fetal ultrasound scanning from eye-tracking and pupillary data of sonographers. Human skill characterization for this clinical task typically creates groupings of clinician skills such as expert and beginner based on the number of years of professional experience; experts typically have more than 10 years and beginners between 0-5 years. In some cases, they also include trainees who are not yet fully-qualified professionals. Prior work has considered eye movements that necessitates separating eye-tracking data into eye movements, such as fixations and saccades. Our method does not use prior assumptions about the relationship between years of experience and does not require the separation of eye-tracking data. Our best performing skill classification model achieves an F1 score of 98% and 70% for expert and trainee classes respectively. We also show that years of experience as a direct measure of skill, is significantly correlated to the expertise of a sonographer.

Original languageEnglish
Pages (from-to)184-198
Number of pages15
JournalProceedings of Machine Learning Research
Volume210
StatePublished - 2023
Event1st Gaze Meets ML Workshop, in conjunction with the 36th Annual Conference on Neural Information Processing Systems, NeurIPS 2022 - New Orleans, United States
Duration: 3 Dec 2022 → …

Funding

FundersFunder number
COCHE
InnoHK-funded Hong Kong Centre for Cerebro-cardiovascular Health Engineering
Oxford Partnership Comprehensive Biomedical Research Centre
Manchester Biomedical Research Centre
NIHR Imperial Biomedical Research Centre

    Keywords

    • Eye-tracking
    • fetal ultrasound
    • skill classification

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