Clinical trial NCT04319055: AI-Assisted Facial Surgical Planning
Official title: Artificial Intelligence-Assisted Facial, Periocular, and Orbital Analysis and Surgical Planning
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Facial Plastic and Reconstructive Surgery
Periocular Diseases
Orbital Diseases
Artificial Intelligence
Keywords: Facial Plastic and Reconstructive Surgery
Periocular Diseases
Orbital Diseases
Artificial Intelligence
Lead sponsor: National Taiwan University Hospital
Sponsor class: OTHER
Collaborators: name: Stanford University
First posted: 2020-03-24
Enrollment: 17932
Sex: ALL
Minimum age (years): 20
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: All data was collected at ophthalmic outpatient clinics of National Taiwan University Hospital.

Brief 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.

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.

Eligibility 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.

Primary outcomes:
measure: The model performance is evaluated by accuracy
timeFrame: Through study completion, an average of 1 year
measure: AUC (Area Under the Curve)
timeFrame: Through study completion, an average of 1 year
measure: ROC (Receiver Operating Characteristics) curve.
timeFrame: Through study completion, an average of 1 year
measure: An Artificial Intelligence Approach to Identifying Facial, Periocular, and Orbital Diseases
timeFrame: Through study completion, an average of 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Taipei
country: Taiwan
facility: National Taiwan University Hospital
Study officials:
affiliation: National Taiwan University Hospital
name: Shu-Lang Liao, MD,MPH, EMBA
role: PRINCIPAL_INVESTIGATOR
References: