Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual FieldNCT03759483
Diagnostic Efficacy of CNN in Differentiation of Visual Field
An observational study of Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual Field, run by Sun Yat-sen University at 1 site in China.
Trial summary
Glaucoma is currently the leading cause of irreversible blindness in the world. The multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, and to assess its utility in the real world. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual Field
- What is being tested?
- AI diagnostic algorithm
- How many people
- 437 participants
- Where
- 1 siteChina
- Study dates
- 15 Mar 2019to 31 Dec 2019
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients from clinics in different eye centers across China. Each subject must be diagnosed based on comprehensive medical tests and medical records. The leading center will read all the medical data to give out diagnosis as the gold standard.
Who may be able to join
- Age≥18;
- Informed consent obtained;
- Diagnosed with specific ocular diseases;
- Able to perform visual field test
Who cannot join
- Incomplete clinical data to support diagnosis
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Differentiation of Glaucomatous Visual Field From Non-glaucomatous Visual Field
Glaucoma is currently the leading cause of irreversible blindness in the world. The multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, and to assess its utility in the real world.
Read the detailed description
Glaucoma is the world's leading cause of irreversible blind, characterized by progressive retinal nerve fiber layer thinning and visual field defects. Visual field test is one of the gold standards for diagnosis and evaluation of progression of glaucoma. However, there is no universally accepted standard for the interpretation of visual field results, which is subjective and requires a large amount of experience. At present, artificial intelligence has achieved the accuracy comparable to human physicians in the interpretation of medical imaging of many different diseases. Previously, we have trained a deep convolutional neural network to read the visual field reports, which has even higher diagnostic efficacy than ophthalmologists. The current multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, compare its performance with ophthalmologists and to assess its utility in the real world.
- Being tested
- AI diagnostic algorithm
- Keywords
- Deep convolutional neural network, Visual field, Glaucoma
- Medical subject headings
- Glaucoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI diagnostic algorithmDiagnostic test
Also called Standard diagnostic procedure
The visual fields collected would be assessed by the algorithm and ophthalmologists independently. The performance of the algorithm and the ophthalmologists would be compared, including accuracy, AUC, sensitivity and specificity.
| Study group | Role | What they receive |
|---|---|---|
| AI group | The visual field reports in this group will be evaluated by the convolutional neural network. Receives: Diagnostic Test: AI diagnostic algorithm | |
| Human group | The visual field reports in this group will be evaluated by 3 ophthalmologists independently. |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC value of convolutional neural network in differentiation of Glaucoma visual field from non-glaucoma visual field
Measured: from Jan 2019 to Jan 2020
Secondary outcomes (1)
Sensitivity and specificity of convolutional neural network in detection of glaucoma visual field
Measured: from Jan 2019 to Jan 2020
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Nov 2018
- Study start15 Mar 2019
- Primary completion31 Dec 2019
- Study completion31 Dec 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual FieldStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe sponsor's other registered studies.
- 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
- Other
- Time perspective
- Other
- Enrolment
- 437 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiulan Zhang, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03759483
- Sponsor's protocol ID
- 2018KYPJ125
- Status last verified
- Jan 2020
- First submitted
- 26 Nov 2018
- Last updated
- 27 Jan 2020
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Jan 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Differentiation of Glaucomatous Visual Field From Non-glaucomatous Visual Field
- ClinicalTrials.gov ID
- NCT03759483
- Acronym / sponsor ID
- 2018KYPJ125
- Keywords
- Deep convolutional neural network, Visual field, Glaucoma
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 437 participants (actual)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 15 Mar 2019 (actual)
- Primary completion
- 31 Dec 2019 (actual)
- Study completion
- 31 Dec 2019 (actual)
- First posted
- 30 Nov 2018 (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.
Glaucoma is currently the leading cause of irreversible blindness in the world. The multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, and to assess its utility in the real world.
Detailed description
Glaucoma is the world's leading cause of irreversible blind, characterized by progressive retinal nerve fiber layer thinning and visual field defects. Visual field test is one of the gold standards for diagnosis and evaluation of progression of glaucoma. However, there is no universally accepted standard for the interpretation of visual field results, which is subjective and requires a large amount of experience. At present, artificial intelligence has achieved the accuracy comparable to human physicians in the interpretation of medical imaging of many different diseases. Previously, we have trained a deep convolutional neural network to read the visual field reports, which has even higher diagnostic efficacy than ophthalmologists. The current multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, compare its performance with ophthalmologists and to assess its utility in the real world.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI group | – | Diagnostic Test: AI diagnostic algorithmThe visual field reports in this group will be evaluated by the convolutional neural network. |
| Human group | – | –The visual field reports in this group will be evaluated by 3 ophthalmologists independently. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients from clinics in different eye centers across China. Each subject must be diagnosed based on comprehensive medical tests and medical records. The leading center will read all the medical data to give out diagnosis as the gold standard.
Key inclusion criteria (4 listed)
- Age≥18;
- Informed consent obtained;
- Diagnosed with specific ocular diseases;
- Able to perform visual field test
Key exclusion criteria (1 listed)
- Incomplete clinical data to support diagnosis
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 |
|---|---|---|
| AI diagnostic algorithmAlso: Standard diagnostic procedureThe visual fields collected would be assessed by the algorithm and ophthalmologists independently. The performance of the algorithm and the ophthalmologists would be compared, including accuracy, AUC, sensitivity and specificity. | Diagnostic test | AI group |
Primary outcome
AUC value of convolutional neural network in differentiation of Glaucoma visual field from non-glaucoma visual field
Time frame: from Jan 2019 to Jan 2020
1 secondary outcome
Sensitivity and specificity of convolutional neural network in detection of glaucoma visual field
Time frame: from Jan 2019 to Jan 2020
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jan 2020.
- Individual participant dataSharing planned: No
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.
The record lists no contact. Use the official record to reach the sponsor.
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 Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual Field, run by Sun Yat-sen University 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 4 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: finished, as of the sponsor's last update on 27 Jan 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC value of convolutional neural network in differentiation of Glaucoma visual field from non-glaucoma visual field (time frame: from Jan 2019 to Jan 2020). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 27 Jan 2020.