Dry eye is one of the most common ocular surface diseases. Its pathogenic factors are related to multiple etiology. Because of the complexity of the pathogenesis of dry eye, the diversity of related examinations, and the inconsistency of symptoms and signs of dry eye patients, the diagnosis of dry eye has higher requirements on the professional technology and examination equipment of ophthalmologists.

The purpose of this study is to establish a case-control cohort of dry eye patients. Multimodal data will be collected from participants, including medical history information, ocular surface disease index scale (OSDI), anterior segment photography, and treatment outcome of dry eye patients. The correlation between the characteristics of anterior segment images and dry eye diagnosis will be explored by artificial intelligence algorithms. The purpose of this study was to develop an artificial intelligence dry eye screening and referral system.

Inclusion Criteria:

1. Subjects whose age are greater than or equal to 18 years old;
2. Subjects who can cooperate with the inspection;
3. Subjects who agree to participate in the study and sign the consent form.

Exclusion Criteria:

1. Subjects who cannot do the inspection.
2. Subjects who suffer from diseases that compromise the inspection.

Subjects who are equal or over 18 years old with or without dry eye symptoms and signs.