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Development and Validation of Deep Neural Networks for Blinking Identification and Classification

NCT04828187 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 8

Last updated 2023-01-04

No results posted yet for this study

Summary

Primary objective of this study is the development and validation of a system of deep neural networks which automatically detects and classifies blinks as "complete" or "incomplete" in image sequences.

Conditions

  • Blinking
  • Deep Learning

Interventions

DIAGNOSTIC_TEST

Comparison of the proposed artificial network with the ground truth

Both eyes will be included for each study participant. Participants watched a 4-10-minute video in standard mesopic environmental lighting conditions at 3.5m viewing distance. Simultaneously, all blinking moves will be recorded through a web infrared camera. The proposed system was tested on the 8 different subjects. Several metrics of blink detection and classification accuracy were calculated against the ground truth, which was generated by 3 independent experts, whose conflicts were resolved by a senior expert. Two independent blink identifications are assumed to be in agreement, if and only if there is sufficient temporal overlapping and the type of blink is the same between the DLED system and the ground truth.

Sponsors & Collaborators

  • University of Thessaly

    collaborator OTHER

  • Democritus University of Thrace

    lead OTHER

Principal Investigators

  • Georgios Labiris, MD,PhD · Department of Ophthalmology, University Hospital of Alexandroupolis, Alexandroupolis, Greece

Eligibility

Min Age
18 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-10-01
Primary Completion
2021-03-10
Completion
2021-03-25

Countries

  • Greece

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04828187 on ClinicalTrials.gov