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AI-Assisted Facial Surgical Planning

National Taiwan University logo

National Taiwan University

Status

Completed

Conditions

Facial Plastic and Reconstructive Surgery
Periocular Diseases
Artificial Intelligence
Orbital Diseases

Study type

Observational

Funder types

Other

Identifiers

NCT04319055
201908066RIND

Details and patient eligibility

About

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.

Full 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.

Enrollment

17,932 patients

Sex

All

Ages

20 to 65 years old

Volunteers

No Healthy Volunteers

Inclusion and exclusion criteria

[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.

Trial contacts and locations

1

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Data sourced from clinicaltrials.gov

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