Deep LearningNCT04592068
AI Classifies Multi-Retinal Diseases
An observational study of Deep Learning and Retinal Diseases, run by Beijing Tongren Hospital at 1 site in China.
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Deep Learning, Retinal Diseases
- What is being tested?
- Retinal multi-diseases diagnosed by DL algorithm and Retinal multi-diseases diagnosed by expert panel
- Type of treatment
- Medical device
- How many people
- 10,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Nov 2020to 1 Dec 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: community derived dataset
Who may be able to join
- fundus photography around 45° field which covers optic disc and macula
- complete patient identification information;
Who cannot join
- incomplete patient identification information
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Wen-Bin Wei
Beijing, Beijing Municipality, 100730
Wen-Bin Wei, MD, weiwenbintr@163.com
Wen-Bin Wei, MD
About this study
Official title: Deep Learning-Based Automated Classification of Multi-Retinal Disease From Fundus Photography
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.
Read the detailed 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.
- Conditions
- Deep Learning, Retinal Diseases
- Being tested
- Retinal multi-diseases diagnosed by DL algorithm, Retinal multi-diseases diagnosed by expert panel
- Medical subject headings
- Retinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Retinal multi-diseases diagnosed by DL algorithmDevice
DL algorithm automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.
Retinal multi-diseases diagnosed by expert panelOther
Expert panel classifies multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.
| Study group | Role | What they receive |
|---|---|---|
| Retinal multi-diseases diagnosed by DL algorithm | Not described Receives: Device: Retinal multi-diseases diagnosed by DL algorithm | |
| Retinal multi-diseases diagnosed by expert panel | Not described Receives: Other: Retinal multi-diseases diagnosed by expert panel |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 week
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.
Measured: 1 week
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.
Measured: 1 week
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.
Measured: 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Oct 2020
- Study start1 Nov 2020
- Primary completion1 Nov 2021 (estimated)
- Study completion1 Dec 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Deep LearningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Beijing Tongren Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Beijing Tulip Partner Technology Co., Ltd, China
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04592068
- Sponsor's protocol ID
- Retinal multi diseases
- Status last verified
- Oct 2020
- First submitted
- 13 Oct 2020
- Last updated
- 11 Dec 2020
- Results posted
- No
- FDA-regulated drug
- Yes
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Gu C, Wang Y, Jiang Y, Xu F, Wang S, Liu R, Yuan W, Abudureyimu N, Wang Y, Lu Y, Li X, Wu T, Dong L, Chen Y, Wang B, Zhang Y, Wei WB, Qiu Q, Zheng Z, Liu D, Chen J. Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases. Br J Ophthalmol. 2024 Feb 21;108(3):424-431. doi: 10.1136/bjo-2022-322940.
PubMed 36878715
Study at a glance
From the registry record, last updated by the sponsor on 11 Dec 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning-Based Automated Classification of Multi-Retinal Disease From Fundus Photography
- ClinicalTrials.gov ID
- NCT04592068
- Acronym / sponsor ID
- Retinal multi diseases
- Conditions
- Deep Learning, Retinal Diseases
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Beijing Tongren HospitalUniversity, hospital or other
- Collaborators
- Beijing Tulip Partner Technology Co., Ltd, China
- Study start
- 1 Nov 2020 (actual)
- Primary completion
- 1 Nov 2021 (estimated)
- Study completion
- 1 Dec 2021 (estimated)
- First posted
- 19 Oct 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed 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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Retinal multi-diseases diagnosed by DL algorithm | – | Device: Retinal multi-diseases diagnosed by DL algorithm |
| Retinal multi-diseases diagnosed by expert panel | – | Other: Retinal multi-diseases diagnosed by expert panel |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- community derived dataset
Key inclusion criteria (2 listed)
- fundus photography around 45° field which covers optic disc and macula
- complete patient identification information;
Key exclusion criteria (1 listed)
- incomplete patient identification information
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Retinal multi-diseases diagnosed by DL algorithmDL algorithm automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. | Device | Retinal multi-diseases diagnosed by DL algorithm |
| Retinal multi-diseases diagnosed by expert panelExpert panel classifies multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. | Other | Retinal multi-diseases diagnosed by expert panel |
Primary outcome
Area under curve
Time frame: 1 week
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.
Sensitivity and specificity
Time frame: 1 week
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.
Positive and negative predictive value
Time frame: 1 week
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.
Accuracy
Time frame: 1 week
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Dec 2020.
- Derived from this study: PubMed 36878715Gu C, Wang Y, Jiang Y, Xu F, Wang S, Liu R, Yuan W, Abudureyimu N, Wang Y, Lu Y, Li X, Wu T, Dong L, Chen Y, Wang B, Zhang Y, Wei WB, Qiu Q, Zheng Z, Liu D, Chen J. Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases. Br J Ophthalmol. 2024 Feb 21;108(3):424-431. doi: 10.1136/bjo-2022-322940.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Deep Learning and Retinal Diseases, run by Beijing Tongren Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 11 Dec 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under curve (time frame: 1 week). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04592068, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Dec 2020.