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

Completed

• Conditions: Blinking; Deep Learning
• Interventions: Diagnostic Test: Comparison of the proposed artificial network with the ground truth

• Registration Number: NCT04828187
• Lead Sponsor: Democritus University of Thrace

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.

Study Timeline

• Study Start: 2020-10-01
• Primary Completion: 2021-03-10
• Study Completion: 2021-03-25
• Study First Submitted: 2021-03-29
• Study First Submitted QC: 2021-03-31
• Study First Posted: 2021-04-01
• Status Verified: 2023-01
• Last Update Submitted: 2023-01-02
• Last Update Posted: 2023-01-04

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 8

Inclusion Criteria

Not provided

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Exclusion Criteria

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

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Study group	Comparison of the proposed artificial network with the ground truth	8 patients aged between 18 to 75 years with Uncorrected Distance Visual Acuity ≥ 5/10

Primary Outcome Measures

Name	Time	Method
First frame of each blink	up to 1 week	The frame in which the upper eyelid starts to move down and cover the cornea
Last frame of each blink	up to 1 week	The frame in which eyelids open fully after a blink
Identification of complete and incomplete blinks	up to 1 week	Complete and incomplete blinks are defined by the "length of palpebral fissure-to-iris diameter" ratio

Secondary Outcome Measures

Name	Time	Method
Length of palpebral fissure of both eyes	up to 1 week	The distance between the upper eyelid margin and the lower eyelid margin (ie. the vertical dimension of the palpebral fissure),
Iris diameter of both eyes	up to 1 week	The horizontal diameter of the iris (ie. the horizontal white-to white distance)

Trial Locations

Locations (2)

Department of Computer Science and Biomedical Informatics, University of Thessaly; 🇬🇷 Lamia, Thessaly, Greece

Department of Ophthalmology, University Hospital of Alexandroupolis; 🇬🇷 Alexandroupolis, Evros, Greece