AI-Assisted Facial Surgical Planning

February 16, 2021 updated by: National Taiwan University Hospital

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.

Study Overview

Status

Completed

Conditions

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 Type

Observational

Enrollment (Actual)

17932

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Taiwan
      • Taipei, Taiwan
        • National Taiwan University Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

20 years to 65 years (ADULT, OLDER_ADULT)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

All data was collected at ophthalmic outpatient clinics of National Taiwan University Hospital.

Description

[Inclusion Criteria]

  1. The participants who were 20-year-old or above,
  2. Surgical informed consent was endorsed by the participants themselves,
  3. Participants who have surgical indications of the oculofacial surgeries, and
  4. The participants who agreed on photograph taking after explanation by the surgeon at outpatient clinics.

[Exclusion Criteria]

  1. The participants who were 19-year-old or under,
  2. The participants who don't have surgical indications of the oculofacial surgeries,
  3. The participants who were designed for minimal invasive treatments, such as Botox or any kind of fillers injection,
  4. The participants who refused photograph taking for any reason, and
  5. The participants who are not available for standard quality of photograph taking, such as bedridden patients.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The model performance is evaluated by accuracy
Time Frame: Through study completion, an average of 1 year
An Artificial Intelligence Approach
Through study completion, an average of 1 year
AUC (Area Under the Curve)
Time Frame: Through study completion, an average of 1 year
An Artificial Intelligence Approach
Through study completion, an average of 1 year
ROC (Receiver Operating Characteristics) curve.
Time Frame: Through study completion, an average of 1 year
An Artificial Intelligence Approach
Through study completion, an average of 1 year
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).
Through study completion, an average of 1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

National Taiwan University Hospital

Collaborators

Stanford University

Investigators

  • Principal Investigator: Shu-Lang Liao, MD,MPH, EMBA, National Taiwan University Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ACTUAL)

January 1, 2009

Primary Completion (ACTUAL)

December 31, 2018

Study Completion (ACTUAL)

July 30, 2019

Study Registration Dates

First Submitted

March 3, 2020

First Submitted That Met QC Criteria

March 23, 2020

First Posted (ACTUAL)

March 24, 2020

Study Record Updates

Last Update Posted (ACTUAL)

February 18, 2021

Last Update Submitted That Met QC Criteria

February 16, 2021

Last Verified

February 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 201908066RIND

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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