Facial Plastic and Reconstructive SurgeryNCT04319055
AI-Assisted Facial Surgical Planning
An observational study of Facial Plastic and Reconstructive Surgery and Periocular Diseases, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
Computer vision using deep learning architecture is broadly used in auto-recognition. In the research, the deep learning model which is trained by categorized single-eye images is applied to achieve the good performance of the model in blepharoptosis auto-diagnosis. Read more about this study
- Who is it for?
- Ages 20 to 65 years
- Conditions the study is for
- Facial Plastic and Reconstructive Surgery, Periocular Diseases, Orbital Diseases
- How many people
- 17,932 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Jan 2009to 30 Jul 2019
- 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 20 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: All data was collected at ophthalmic outpatient clinics of National Taiwan University Hospital.
[Inclusion Criteria]
- The participants who were 20-year-old or above,
- Surgical informed consent was endorsed by the participants themselves,
- Participants who have surgical indications of the oculofacial surgeries, and
- The participants who agreed on photograph taking after explanation by the surgeon at outpatient clinics.
[Exclusion Criteria]
- The participants who were 19-year-old or under,
- The participants who don't have surgical indications of the oculofacial surgeries,
- The participants who were designed for minimal invasive treatments, such as Botox or any kind of fillers injection,
- The participants who refused photograph taking for any reason, and
- The participants who are not available for standard quality of photograph taking, such as bedridden patients.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence-Assisted Facial, Periocular, and Orbital Analysis and Surgical Planning
Computer vision using deep learning architecture is broadly used in auto-recognition. In the research, the deep learning model which is trained by categorized single-eye images is applied to achieve the good performance of the model in blepharoptosis auto-diagnosis.
Read the detailed description
This auto-diagnosis system of blepharoptosis using machine learning architecture will assist in telemedicine, such as early screening of childhood ptosis for prompt referral and treatment. People could use this software via mobile devices to get a primitive diagnosis before they reach the physicians. Furthermore, in primary health care, where there is no oculoplastic surgeon, the software could assist primary care physicians or general ophthalmologists, in identifying the need for a referral.
- Conditions
- Facial Plastic and Reconstructive Surgery, Periocular Diseases, Orbital Diseases, Artificial Intelligence
- Keywords
- Facial Plastic and Reconstructive Surgery, Periocular Diseases, Orbital Diseases, Artificial Intelligence
- Medical subject headings
- Orbital Diseases
What is being measured
The primary outcome is the main question the study is designed to answer.
The model performance is evaluated by accuracy
An Artificial Intelligence Approach
Measured: Through study completion, an average of 1 year
AUC (Area Under the Curve)
An Artificial Intelligence Approach
Measured: Through study completion, an average of 1 year
ROC (Receiver Operating Characteristics) curve.
An Artificial Intelligence Approach
Measured: Through study completion, an average of 1 year
An Artificial Intelligence Approach to Identifying Facial, Periocular, and Orbital Diseases
The model interpretability is accessed by Grad-CAM (Class Activation Maps).
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Mar 2020
- Study start1 Jan 2009
- Primary completion31 Dec 2018
- Study completion30 Jul 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Facial Plastic and Reconstructive SurgeryStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 17,932 participants (actual)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Stanford University
- Responsible party
- Sponsor
- Investigators
- Shu-Lang Liao, MD,MPH, EMBA, Principal investigator, National Taiwan University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04319055
- Sponsor's protocol ID
- 201908066RIND
- Status last verified
- Feb 2021
- First submitted
- 3 Mar 2020
- Last updated
- 18 Feb 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Feb 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Assisted Facial, Periocular, and Orbital Analysis and Surgical Planning
- ClinicalTrials.gov ID
- NCT04319055
- Acronym / sponsor ID
- 201908066RIND
- Conditions
- Facial Plastic and Reconstructive Surgery, Periocular Diseases, Orbital Diseases, Artificial Intelligence
- Keywords
- Facial Plastic and Reconstructive Surgery, Periocular Diseases, Orbital Diseases, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 17,932 participants (actual)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Collaborators
- Stanford University
- Study start
- 1 Jan 2009 (actual)
- Primary completion
- 31 Dec 2018 (actual)
- Study completion
- 30 Jul 2019 (actual)
- First posted
- 24 Mar 2020 (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.
Computer vision using deep learning architecture is broadly used in auto-recognition. In the research, the deep learning model which is trained by categorized single-eye images is applied to achieve the good performance of the model in blepharoptosis auto-diagnosis.
Detailed description
This auto-diagnosis system of blepharoptosis using machine learning architecture will assist in telemedicine, such as early screening of childhood ptosis for prompt referral and treatment. People could use this software via mobile devices to get a primitive diagnosis before they reach the physicians. Furthermore, in primary health care, where there is no oculoplastic surgeon, the software could assist primary care physicians or general ophthalmologists, in identifying the need for a referral.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- 20 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All data was collected at ophthalmic outpatient clinics of National Taiwan University Hospital.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The model performance is evaluated by accuracy
Time frame: Through study completion, an average of 1 year
An Artificial Intelligence Approach
AUC (Area Under the Curve)
Time frame: Through study completion, an average of 1 year
An Artificial Intelligence Approach
ROC (Receiver Operating Characteristics) curve.
Time frame: Through study completion, an average of 1 year
An Artificial Intelligence Approach
An Artificial Intelligence Approach to Identifying Facial, Periocular, and Orbital Diseases
Time frame: Through study completion, an average of 1 year
The model interpretability is accessed by Grad-CAM (Class Activation Maps).
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 18 Feb 2021.
- Individual participant dataSharing planned: No
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.
Shu-Lang Liao, MD,MPH, EMBA principal investigator
National Taiwan University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Facial Plastic and Reconstructive Surgery and Periocular Diseases, run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
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 18 Feb 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. 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?
The model performance is evaluated by accuracy (time frame: Through study completion, an average of 1 year). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 18 Feb 2021.