Clinical trial NCT04828187: Development and Validation of Deep Neural Networks for Blinking Identification and Classification
Official title: Development and Validation of Deep Neural Networks for Blinking Identification and Classification
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Blinking
Deep Learning
Keywords: deep learning
DeepLabv3+
iris and sclera segmentation
eye blink detection
complete and incomplete blink classification
Lead sponsor: Democritus University of Thrace
Sponsor class: OTHER
Collaborators: name: University of Thessaly
First posted: 2021-04-01
Enrollment: 8
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients aged from 18 to 75 years with Uncorrected Distance Visual Acuity ≥ 5/10

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.

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

Primary outcomes:
measure: Identification of complete and incomplete blinks
timeFrame: up to 1 week
measure: First frame of each blink
timeFrame: up to 1 week
measure: Last frame of each blink
timeFrame: up to 1 week
Secondary outcomes:
measure: Length of palpebral fissure of both eyes
timeFrame: up to 1 week
measure: Iris diameter of both eyes
timeFrame: up to 1 week
Other outcomes:
Locations:
city: Alexandroupolis
country: Greece
facility: Department of Ophthalmology, University Hospital of Alexandroupolis
state: Evros
city: Lamia
country: Greece
facility: Department of Computer Science and Biomedical Informatics, University of Thessaly
state: Thessaly
Study officials:
affiliation: Department of Ophthalmology, University Hospital of Alexandroupolis, Alexandroupolis, Greece
name: Georgios Labiris, MD,PhD
role: STUDY_CHAIR
References:
citation: 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.