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.

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.

[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.

All data was collected at ophthalmic outpatient clinics of National Taiwan University Hospital.