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OpenTrials
Completed

NCT Number: NCT04319055

AI-Assisted Facial 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.

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Key information

Conditions

Facial Plastic and Reconstructive Surgery Artificial Intelligence Eye Diseases Orbital Diseases Periocular Diseases

Age range

20 year–65 year

Sex eligibility

All sexes

Study type

Observational

Primary location

National Taiwan University Hospital

Taipei, Taiwan

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

[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.

Treatment and study plan

Primary outcomes

  1. The model performance is evaluated by accuracy

    Time frame: Through study completion, an average of 1 year

    An Artificial Intelligence Approach

  2. AUC (Area Under the Curve)

    Time frame: Through study completion, an average of 1 year

    An Artificial Intelligence Approach

  3. ROC (Receiver Operating Characteristics) curve.

    Time frame: Through study completion, an average of 1 year

    An Artificial Intelligence Approach

  4. 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).

Sponsors and collaborators

Lead sponsor

National Taiwan University Hospital

Other

Collaborators

  • Stanford University

Registry information

Official study title

Artificial Intelligence-Assisted Facial, Periocular, and Orbital Analysis and Surgical Planning

Important dates

Study start
2009
Primary completion
2018
Study completion
2019
First posted
Mar 24, 2020
Registry last updated
Feb 18, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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