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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT04592068
Other study ID # Retinal multi diseases
Secondary ID
Status Recruiting
Phase
First received
Last updated
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
Health authority
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

Study Design


Related Conditions & MeSH terms


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
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