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Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging

Authors
  • Sharma, Harshita1
  • Drukker, Lior2
  • Papageorghiou, Aris T.2
  • Noble, J. Alison1
  • 1 Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
  • 2 Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
Type
Published Article
Journal
Computers in Biology and Medicine
Publisher
Elsevier
Publication Date
Aug 01, 2021
Volume
135
Identifiers
DOI: 10.1016/j.compbiomed.2021.104589
PMID: 34198044
PMCID: PMC8404042
Source
PubMed Central
Keywords
Disciplines
  • Article
License
Unknown

Abstract

• Machine learning-based pupillary response analysis is performed to assess operator cognitive workload in clinical ultrasound. • A systematic multi-modal data analysis pipeline is proposed using eye-tracking, pupillometry, and sonography data science. • Pertinent challenges of natural or real-world clinical datasets are addressed. • Pupillary responses around event triggers, different ultrasonographic tasks, and different operator experiences are studied. • Machine learning models are learnt to classify undertaken tasks or operator expertise from pupillometric time-series data.

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