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

Clare Teng, Lior Drukker, Aris T Papageorghiou, J Alison Noble
Proceedings of The 1st Gaze Meets ML workshop, PMLR 210:184-198, 2023.

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 necessi- tates 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 experi- ence 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 re- spectively. We also show that years of experience as a direct measure of skill, is significantly correlated to the expertise of a sonographer.

Cite this Paper

BibTeX
@InProceedings{pmlr-v210-teng23a, title = {Skill, or Style? Classification of Fetal Sonography Eye-Tracking Data}, author = {Teng, Clare and Drukker, Lior and Papageorghiou, Aris T and Noble, J Alison}, booktitle = {Proceedings of The 1st Gaze Meets ML workshop}, pages = {184--198}, year = {2023}, editor = {Lourentzou, Ismini and Wu, Joy and Kashyap, Satyananda and Karargyris, Alexandros and Celi, Leo Anthony and Kawas, Ban and Talathi, Sachin}, volume = {210}, series = {Proceedings of Machine Learning Research}, month = {03 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v210/teng23a/teng23a.pdf}, url = {https://proceedings.mlr.press/v210/teng23a.html}, 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 necessi- tates 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 experi- ence 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 re- spectively. We also show that years of experience as a direct measure of skill, is significantly correlated to the expertise of a sonographer.} }
Endnote
%0 Conference Paper %T Skill, or Style? Classification of Fetal Sonography Eye-Tracking Data %A Clare Teng %A Lior Drukker %A Aris T Papageorghiou %A J Alison Noble %B Proceedings of The 1st Gaze Meets ML workshop %C Proceedings of Machine Learning Research %D 2023 %E Ismini Lourentzou %E Joy Wu %E Satyananda Kashyap %E Alexandros Karargyris %E Leo Anthony Celi %E Ban Kawas %E Sachin Talathi %F pmlr-v210-teng23a %I PMLR %P 184--198 %U https://proceedings.mlr.press/v210/teng23a.html %V 210 %X 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 necessi- tates 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 experi- ence 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 re- spectively. We also show that years of experience as a direct measure of skill, is significantly correlated to the expertise of a sonographer.
APA
Teng, C., Drukker, L., Papageorghiou, A.T. & Noble, J.A.. (2023). Skill, or Style? Classification of Fetal Sonography Eye-Tracking Data. Proceedings of The 1st Gaze Meets ML workshop, in Proceedings of Machine Learning Research 210:184-198 Available from https://proceedings.mlr.press/v210/teng23a.html.

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