AI-Assisted Facial Surgical Planning

Completed

• Conditions: Facial Plastic and Reconstructive Surgery; Orbital Diseases; Artificial Intelligence; Periocular Diseases

• Registration Number: NCT04319055
• Lead Sponsor: 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.

Study Timeline

• Study Start: 2009-01-01
• Primary Completion: 2018-12-31
• Study Completion: 2019-07-30
• Study First Submitted: 2020-03-03
• Study First Submitted QC: 2020-03-23
• Study First Posted: 2020-03-24
• Status Verified: 2021-02
• Last Update Submitted: 2021-02-16
• Last Update Posted: 2021-02-18

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 17932

Inclusion Criteria

Not provided

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Exclusion Criteria

Not provided

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

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

Trial Locations

Locations (1)

National Taiwan University Hospital; 🇨🇳 Taipei, Taiwan