AI Classifies Multi-Retinal Diseases

Retinal Diseases Clinical Trial

Official title:

Deep Learning-Based Automated Classification of Multi-Retinal Disease From Fundus Photography

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04592068
• Other study ID #: Retinal multi diseases
• Status: Recruiting
• Start date: November 1, 2020
• Est. completion date: December 1, 2021

Study information

• Verified date: October 2020
• Source: Beijing Tongren Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The objective of this study is to establish deep learning (DL) algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. The effectiveness and accuracy of the established algorithm will be evaluated in community derived dataset.

Description:

Retinal diseases seriously threaten vision and quality of life, but they often develop insidiously. To date, deep learning (DL) algorithms have shown high prospects in biomedical science, particularly in the diagnosis of ocular diseases, such as diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, glaucoma, and papilledema. However, there is still a lack of a single algorithm that can classify multi-diseases from fundus photography.

This cross-sectional study will establish a DL algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. We will use the receiver operating characteristic (ROC) curve to examine the ability of recognition and classification of diseases. Taken the results of the expert panel as the gold standard, we will use the evaluation indexes, such as sensitivity, specificity, accuracy, positive predictive value, negative predictive value, etc, to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 10000
• Est. completion date: December 1, 2021
• Est. primary completion date: November 1, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• fundus photography around 45° field which covers optic disc and macula

• complete patient identification information;

Exclusion Criteria:

• incomplete patient identification information

Related Conditions & MeSH terms

• Deep Learning
• Retinal Diseases

Intervention

Device:
• Retinal multi-diseases diagnosed by DL algorithm
DL algorithm automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.

Other:
• Retinal multi-diseases diagnosed by expert panel
Expert panel classifies multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.

Locations

Country	Name	City	State
China	Wen-Bin Wei	Beijing	Beijing

Sponsors (2)

Lead Sponsor	Collaborator
Beijing Tongren Hospital	Beijing Tulip Partner Technology Co., Ltd, China

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Area under curve	We will use the receiver operating characteristic (ROC) curve to examine the ability of recognition and classification of diseases. Taken the results of the expert panel as the gold standard, we will use the area under curve to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.	1 week
Primary	Sensitivity and specificity	Taken the results of the expert panel as the gold standard, we will use sensitivity and specificity to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.	1 week
Primary	Positive and negative predictive value	Taken the results of the expert panel as the gold standard, we will use positive and negative predictive value to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.	1 week
Primary	Accuracy	Taken the results of the expert panel as the gold standard, we will use accuracy to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.	1 week