Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

300 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-02-01

Study Completion Date

2020-07-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

Slit-lamp images are widely used in ophthalmology for the detection of cataract, keratopathy and other anterior segment disorders. In real-world practice, the quality of slit-lamp images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of slit-lamp images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis

NCT04289064

Retinal Diseases Artificial Intelligence

UNKNOWN

Validation of the Utility of Ophthalmology Intelligent Diagnostic System

NCT03499145

Ophthalmopathy Artificial Intelligence

COMPLETED

The Clinical Application of Artificial Intelligent(AI) Visual Inspection System

NCT03310216

Ophthalmology

UNKNOWN NA

Validation of the Utility of an Intelligent Visual Acuity Diagnostic System for Children

NCT03766737

Ophthalmopathy Artificial Intelligence

COMPLETED NA

Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images

NCT04213430

Ophthalmological Disorder

UNKNOWN

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Anterior Segment Disorders Artificial Intelligence

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Slit-lamp image quality assessment

Device: an artificial intelligence system for quality assessment of slit-lamp images. These patients are enrolled in primary healthcare units or the AI clinic at Zhongshan Ophthalmic Center

Taking slit-lamp images

Intervention Type DEVICE

The participant only needs to take several slit-lamp images as usual.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Taking slit-lamp images

The participant only needs to take several slit-lamp images as usual.

Intervention Type DEVICE

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Patients should be aware of the contents and signed for the informed consent.

Exclusion Criteria

* 1\. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths.
* 2\. Patients who do not agree to sign informed consent.

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes