AI-Assisted Facial Surgical Planning

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Facial Plastic and Reconstructive Surgery, Periocular Diseases, Orbital Diseases, Artificial Intelligence

Participation Criteria
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Eligibility Criteria
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[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.

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The model performance is evaluated by accuracy | An Artificial Intelligence Approach | Through study completion, an average of 1 year | 
| AUC (Area Under the Curve) | An Artificial Intelligence Approach | Through study completion, an average of 1 year | 
| ROC (Receiver Operating Characteristics) curve. | An Artificial Intelligence Approach | Through study completion, an average of 1 year | 
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Facial Plastic and Reconstructive Surgery, Periocular Diseases, Orbital Diseases, Artificial Intelligence