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

Deep Learning Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04828187
• Other study ID #: ES2/Th15/25-2-2021
• Status: Completed
• Start date: October 1, 2020
• Est. completion date: March 25, 2021

Study information

• Verified date: January 2023
• Source: Democritus University of Thrace
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 8
• Est. completion date: March 25, 2021
• Est. primary completion date: March 10, 2021
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

Inclusion Criteria

• Uncorrected Distance Visual Acuity above 6/12

Exclusion Criteria:

• corneal opacities

• age-related macular degeneration

• diagnosis of psychiatric diseases

• former eyelid surgery

Related Conditions & MeSH terms

• Blinking
• Deep Learning

Intervention

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.

Locations

Country	Name	City	State
Greece	Department of Ophthalmology, University Hospital of Alexandroupolis	Alexandroupolis	Evros
Greece	Department of Computer Science and Biomedical Informatics, University of Thessaly	Lamia	Thessaly

Sponsors (2)

Lead Sponsor	Collaborator
Democritus University of Thrace	University of Thessaly

Country where clinical trial is conducted

Greece, 

References & Publications (1)

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Identification of complete and incomplete blinks	Complete and incomplete blinks are defined by the "length of palpebral fissure-to-iris diameter" ratio	up to 1 week
Primary	First frame of each blink	The frame in which the upper eyelid starts to move down and cover the cornea	up to 1 week
Primary	Last frame of each blink	The frame in which eyelids open fully after a blink	up to 1 week
Secondary	Length of palpebral fissure of both eyes	The distance between the upper eyelid margin and the lower eyelid margin (ie. the vertical dimension of the palpebral fissure),	up to 1 week
Secondary	Iris diameter of both eyes	The horizontal diameter of the iris (ie. the horizontal white-to white distance)	up to 1 week