The Development of Artificial Intelligence Dry Eye Screening and Referral System
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Dry Eye
- Sponsor
- Sun Yat-sen University
- Enrollment
- 518
- Locations
- 1
- Primary Endpoint
- Area under the curve (severe)
- Last Updated
- 4 years ago
Overview
Brief Summary
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.
Investigators
Haotian Lin
Professor
Sun Yat-sen University
Eligibility Criteria
Inclusion Criteria
- •Subjects whose age are greater than or equal to 18 years old;
- •Subjects who can cooperate with the inspection;
- •Subjects who agree to participate in the study and sign the consent form.
Exclusion Criteria
- •Subjects who cannot do the inspection.
- •Subjects who suffer from diseases that compromise the inspection.
Outcomes
Primary Outcomes
Area under the curve (severe)
Time Frame: up to 1 month
AUC values for predicting whether subject need to be referral or not.
Secondary Outcomes
- Area under the curve (each group)(up to 1 month)
- Accuracy, true positive rate, and true negative rate(up to 1 month)