BlinkingNCT04828187
Development and Validation of Deep Neural Networks for Blinking Identification and Classification
An observational study of Blinking and Deep Learning, run by Democritus University of Thrace at 2 sites in Greece.
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. Read more about this study
- Who is it for?
- Ages 18 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Blinking, Deep Learning
- What is being tested?
- Comparison of the proposed artificial network with the ground truth
- How many people
- 8 participants
- Where
- 2 sitesGreece
- Study dates
- 1 Oct 2020to 25 Mar 2021
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients aged from 18 to 75 years with Uncorrected Distance Visual Acuity ≥ 5/10
Who may be able to join
- Uncorrected Distance Visual Acuity above 6/12
Who cannot join
- corneal opacities
- age-related macular degeneration
- diagnosis of psychiatric diseases
- former eyelid surgery
Trial locations and contacts
2 sites in 1 country.
Greece2 sites
Department of Ophthalmology, University Hospital of Alexandroupolis
Alexandroupoli, Evros, 68100
Department of Computer Science and Biomedical Informatics, University of Thessaly
Lamia, Thessaly, 35100
About this study
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.
Read the 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
- Being tested
- Comparison of the proposed artificial network with the ground truth
- Keywords
- deep learning, DeepLabv3+, iris and sclera segmentation, eye blink detection, complete and incomplete blink classification
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Comparison of the proposed artificial network with the ground truthDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Study group | 8 patients aged between 18 to 75 years with Uncorrected Distance Visual Acuity ≥ 5/10 Receives: Diagnostic Test: Comparison of the proposed artificial network with the ground truth |
What is being measured
The primary outcome is the main question the study is designed to answer.
Identification of complete and incomplete blinks
Complete and incomplete blinks are defined by the "length of palpebral fissure-to-iris diameter" ratio
Measured: up to 1 week
First frame of each blink
The frame in which the upper eyelid starts to move down and cover the cornea
Measured: up to 1 week
Last frame of each blink
The frame in which eyelids open fully after a blink
Measured: up to 1 week
Secondary outcomes (2)
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),
Measured: up to 1 week
Iris diameter of both eyes
The horizontal diameter of the iris (ie. the horizontal white-to white distance)
Measured: up to 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Apr 2021
- Study start1 Oct 2020
- Primary completion10 Mar 2021
- Study completion25 Mar 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for BlinkingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 8 participants (actual)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Democritus University of Thrace
- Sponsor type
- University, hospital or other
- Collaborators
- University of Thessaly
- Responsible party
- Principal investigator, Georgios Labiris, Democritus University of Thrace
- Investigators
- Georgios Labiris, MD,PhD, Study chair, Department of Ophthalmology, University Hospital of Alexandroupolis, Alexandroupolis, Greece
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04828187
- Sponsor's protocol ID
- ES2/Th15/25-2-2021
- Status last verified
- Jan 2023
- First submitted
- 29 Mar 2021
- Last updated
- 4 Jan 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 35213320
Study at a glance
From the registry record, last updated by the sponsor on 4 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of Deep Neural Networks for Blinking Identification and Classification
- ClinicalTrials.gov ID
- NCT04828187
- Acronym / sponsor ID
- ES2/Th15/25-2-2021
- Conditions
- Blinking, Deep Learning
- Keywords
- deep learning, DeepLabv3+, iris and sclera segmentation, eye blink detection, complete and incomplete blink classification
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 8 participants (actual)
- Lead sponsor
- Democritus University of ThraceUniversity, hospital or other
- Collaborators
- University of Thessaly
- Study start
- 1 Oct 2020 (actual)
- Primary completion
- 10 Mar 2021 (actual)
- Study completion
- 25 Mar 2021 (actual)
- First posted
- 1 Apr 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Study group | – | Diagnostic Test: Comparison of the proposed artificial network with the ground truth8 patients aged between 18 to 75 years with Uncorrected Distance Visual Acuity ≥ 5/10 |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients aged from 18 to 75 years with Uncorrected Distance Visual Acuity ≥ 5/10
Key inclusion criteria (1 listed)
- Uncorrected Distance Visual Acuity above 6/12
Key exclusion criteria (4 listed)
- corneal opacities
- age-related macular degeneration
- diagnosis of psychiatric diseases
- former eyelid surgery
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Comparison of the proposed artificial network with the ground truthBoth 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. | Diagnostic test | Study group |
Primary outcome
Identification of complete and incomplete blinks
Time frame: up to 1 week
Complete and incomplete blinks are defined by the "length of palpebral fissure-to-iris diameter" ratio
First frame of each blink
Time frame: up to 1 week
The frame in which the upper eyelid starts to move down and cover the cornea
Last frame of each blink
Time frame: up to 1 week
The frame in which eyelids open fully after a blink
2 secondary outcomes
Length of palpebral fissure of both eyes
Time frame: up to 1 week
Iris diameter of both eyes
Time frame: up to 1 week
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jan 2023.
- Background: PubMed 35213320Nousias 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.
- Individual participant dataSharing planned: Undecided
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Georgios Labiris, MD,PhD study chair
Department of Ophthalmology, University Hospital of Alexandroupolis, Alexandroupolis, Greece
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Blinking and Deep Learning, run by Democritus University of Thrace at 2 sites in Greece.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 4 Jan 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Greece. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Identification of complete and incomplete blinks (time frame: up to 1 week). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04828187, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Jan 2023; AzTrials last refreshed its copy on 31 Oct 2026.