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OpenTrials
Completed

NCT Number: NCT04413370

Dry Eye Screening and Referral System

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.

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Key information

Conditions

Dry Eye Dry Eye Syndromes Eye Diseases Lacrimal Apparatus Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Zhongshan Ophthalmic Center

Guangzhou, Guangdong, 510632, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Dry eye diagnostic test

Diagnostic Test

The artificial intelligent dry eye screening platform

Primary outcomes

  1. 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

  1. Area under the curve (each group)

    Time frame: up to 1 month

    AUC values for predicting whether subject can be accurately grouped into each of the four groups.

  2. Accuracy, true positive rate, and true negative rate

    Time frame: up to 1 month

    The performance of this artificial platform.

Sponsors and collaborators

Lead sponsor

Sun Yat-sen University

Other

Registry information

Official study title

The Development of Artificial Intelligence Dry Eye Screening and Referral System

Important dates

Study start
2020
Primary completion
2024
Study completion
2024
First posted
Jun 2, 2020
Registry last updated
Sep 23, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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