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

NCT ID: NCT04828187

Last Updated: 2023-01-04

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 8 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-10-01
• Study Completion Date: 2021-03-25

Brief 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.

Detailed Description

This method is based on iris and sclera segmentation in both eyes from the acquired images, using state of the art deep learning encoder-decoder neural architectures (DLED). The sequence of the segmented frames is post-processed to calculate the distance between the eyelids of each eye (palpebral fissure) and the corresponding iris diameter. Theses quantities are temporally filtered and their fraction is subject to adaptive thresholding to identify blinks and determine their type, independently for each eye. The two DLEDs were trained with manually segmented images and the post-process was parameterized using a 4-minute video. After DLED training, the proposed system was tested on 8 different subjects, each one with a 4-10-minute video. 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.

Conditions

Blinking; Deep Learning

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Study group

8 patients aged between 18 to 75 years with Uncorrected Distance Visual Acuity ≥ 5/10

Comparison of the proposed artificial network with the ground truth

Intervention Type: DIAGNOSTIC_TEST

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.

Interventions

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Uncorrected Distance Visual Acuity above 6/12

Exclusion Criteria

• corneal opacities
• age-related macular degeneration
• diagnosis of psychiatric diseases
• former eyelid surgery

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

University of Thessaly

OTHER

Sponsor Role: collaborator

Democritus University of Thrace

OTHER

Sponsor Role: lead

Responsible Party

Georgios Labiris

Associate Professor of Ophthalmology

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Georgios Labiris, MD,PhD

Role: STUDY_CHAIR

Department of Ophthalmology, University Hospital of Alexandroupolis, Alexandroupolis, Greece

Locations

Department of Ophthalmology, University Hospital of Alexandroupolis

Alexandroupoli, Evros, Greece

Department of Computer Science and Biomedical Informatics, University of Thessaly

Lamia, Thessaly, Greece

Countries

Greece

References

Nousias G, Panagiotopoulou EK, Delibasis K, Chaliasou AM, Tzounakou AM, Labiris G. Video-Based Eye Blink Identification and Classification. IEEE J Biomed Health Inform. 2022 Jul;26(7):3284-3293. doi: 10.1109/JBHI.2022.3153407. Epub 2022 Jul 1.

Reference Type: BACKGROUND

PMID: 35213320 (View on PubMed)

Other Identifiers

ES2/Th15/25-2-2021

Identifier Type: -

Identifier Source: org_study_id