AI-Assisted Facial Surgical Planning

Artificial Intelligence Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04319055
• Other study ID #: 201908066RIND
• Status: Completed
• Start date: January 1, 2009
• Est. completion date: July 30, 2019

Study information

• Verified date: February 2021
• Source: National Taiwan University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 17932
• Est. completion date: July 30, 2019
• Est. primary completion date: December 31, 2018
• Accepts healthy volunteers: No
• Gender: All
• Age group: 20 Years to 65 Years

Eligibility

[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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Facial Plastic and Reconstructive Surgery
• Facies
• Orbital Diseases
• Periocular Diseases

Locations

Country	Name	City	State
Taiwan	National Taiwan University Hospital	Taipei

Sponsors (2)

Lead Sponsor	Collaborator
National Taiwan University Hospital	Stanford University

Country where clinical trial is conducted

Taiwan, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The model performance is evaluated by accuracy	An Artificial Intelligence Approach	Through study completion, an average of 1 year
Primary	AUC (Area Under the Curve)	An Artificial Intelligence Approach	Through study completion, an average of 1 year
Primary	ROC (Receiver Operating Characteristics) curve.	An Artificial Intelligence Approach	Through study completion, an average of 1 year
Primary	An Artificial Intelligence Approach to Identifying Facial, Periocular, and Orbital Diseases	The model interpretability is accessed by Grad-CAM (Class Activation Maps).	Through study completion, an average of 1 year