Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging

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

2000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-11-01

Study Completion Date

2022-12-25

Brief Summary

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

This prospective multicenter study will evaluate the efficacy of a real-time artificial intelligence system for detecting multiple ocular fundus lesions by ultra-widefield fundus imaging in real-world settings.

Related Clinical Trials

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

AI Classifies Multi-Retinal Diseases

NCT04592068

Deep Learning Retinal Diseases

UNKNOWN

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

NCT04289064

Retinal Diseases Artificial Intelligence

UNKNOWN

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

NCT04213430

Ophthalmological Disorder

UNKNOWN

Real-world of AI in Diagnosing Retinal Diseases

NCT05981950

Artificial Intelligence Retinal Diseases

RECRUITING

Multi-modal Imaging and Artificial Intelligence Diagnostic System for Multi-level Clinical Application

NCT03899623

Diabete Mellitus Diabetic Retinopathy

UNKNOWN

Detailed Description

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

The ocular fundus can show signs of both ocular diseases (e.g., lattice degeneration, retinal detachment and glaucoma) and systemic diseases (e.g., hypertension, diabetes and leukemia). The routine fundus examination is conducive for early detection of these diseases. However, manual conducting fundus examination needs an experienced retina ophthalmologist, and is time-consuming and labor-intensive, which is difficult for its routine implementation on large scale.

This study will develop an artificial intelligence system integrating with ultra-widefield fundus imaging to automatically screen for multiple ocular fundus lesions in real time and evaluate its performance in different real-world settings. The efficacy of the system will compare to the final diagnoses of each participant made by experienced ophthalmologists.

Conditions

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

Artificial Intelligence Diagnostic Imaging Abnormality of the Fundus Diagnostic Screening Programs

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.