ArticleScientific reports2022
Analysis of smooth pursuit eye movements in a clinical context by tracking the target and eyes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Integrating Multi-Task Eye Tracking and Interpretable Machine Learning for High-Accuracy Screening of Amblyopia in Pediatric Populations.Journal of eye movement research · 2026Article
- Technical validation of a virtual reality-based eye tracker for neuro-ophthalmic assessment: a reliability and reproducibility study.Scientific reports · 2026Article
- A Comprehensive Framework for Eye Tracking: Methods, Tools, Applications, and Cross-Platform Evaluation.Journal of eye movement research · 2025Review
- Ethological computational psychiatry: Challenges and opportunities.Current opinion in neurobiology · 2024Review
- Precision Concussion Management: Approaches to Quantifying Head Injury Severity and Recovery.Brain sciences · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the evaluation of smooth pursuit eye movements (SPEMs), recording the stimulus onset time is mandatory. In the laboratory, the stimulus onset time is recorded by electrical signal or programming, and video-oculography (VOG) and the visual stimulus are synchronized. Nevertheless, because the examiner must manually move the fixation target, recording the stimulus onset time is challenging in daily clinical practice. Thus, this study aimed to develop an algorithm for evaluating SPEMs while testing the nine-direction eye movements without recording the stimulus onset time using VOG and deep learning-based object detection (single-shot multibox detector), which can predict the location and types of objects in a single image. The algorithm of peak fitting-based detection correctly classified the directions of target orientation and calculated the latencies and gains within the normal range while testing the nine-direction eye movements in healthy individuals. These findings suggest that the algorithm of peak fitting-based detection has sufficient accuracy for the automatic evaluation of SPEM in clinical settings.
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Registered trials
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