Ophthalmological DisorderNCT05622565
Explainable Ocular Fundus Diseases Report Generation System
An observational study of Ophthalmological Disorder and Image, Body, run by Sun Yat-sen University at 1 site in China.
Trial summary
To establish a deep learning system of various ocular fundus disease analytics based on the results of multimodal examination images. The system can analyze multimodal ocular fundus images, make diagnoses and generate corresponding reports. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Ophthalmological Disorder, Image, Body
- What is being tested?
- Various modalities of ocular fundus imaging
- How many people
- 15,000 participants
- Where
- 1 siteChina
- Study dates
- Jan 2011to Jul 2024
- 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
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Ocular fundus images were collected from different health care institutes all over China and from other countries.
Who may be able to join
- The quality of multimodal ocular fundus disease examination images and corresponding reports should be clinically acceptable.
Who cannot join
- Reports with key information missing.
- Images with severe image resolution reductions, blur or artifacts were excluded from further analysis.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Zhognshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510000
Yingfeng Zheng, M.D, Ph.D, +8613922286455, zhyfeng@mail.sysu.edu.cn
About this study
Official title: Explainable Multimodal Deep Neural Networks for Identifying Ocular Fundus Diseases and Report Generation
To establish a deep learning system of various ocular fundus disease analytics based on the results of multimodal examination images. The system can analyze multimodal ocular fundus images, make diagnoses and generate corresponding reports.
Read the detailed description
The ocular fundus is the only part of the human body that can directly see the blood vessel microcirculation and nerve tissue. Through various imaging tests, including Color Fundus Photograph (CFP), Optical Coherence Tomography (OCT), Fluorescein Fundus Angiography (FFA) and Indocyanine Green Angiography (ICGA), etc., it is possible to statically overview or dynamically observe the retina and choroid, the condition of blood vessels and nerves, and comprehensive diagnosis of the disease. The screening, interpreting and accurate diagnosis of ocular fundus diseases are crucial for disease prevention, control and precise treatment. However, due to the variety of fundus examination methods, and the complexity and professionalism of the examination, there is a lack of fundus specialists who have sufficient clinical experience and knowledge to interpret fundus examinations. With the continuous development of artificial intelligence (AI) in diagnosing fundus diseases, various modalities of imaging examination methods are gradually applied to the development of fundus disease diagnosis systems. Moreover, medical images often come with corresponding reports, which are mostly generated by clinicians' or radiologists' experience.
Here, we are establishing a fundus disease diagnosis and report-generating system based on cross-modal ocular fundus imaging examinations, and fundus lesions were visualized at the same time. Multi-center data verification will also be conducted. The results of the research will assist in fundus lesions diagnosis and imaging reports generation. We hope this could popularize more complex fundus imaging examination methods to society, and help improve the early diagnosis and treatment of fundus lesions that cause blindness.
- Conditions
- Ophthalmological Disorder, Image, Body
- Being tested
- Various modalities of ocular fundus imaging
- Keywords
- Retinal disease, Choroidal disease, Deep learning
- Medical subject headings
- Eye Diseases, Retinal Diseases, Choroid Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Various modalities of ocular fundus imagingDiagnostic test
Through various modalities of ocular fundus imaging, combining with clinical data and the experience of clinicians to diagnose different fundus diseases.
| Study group | Role | What they receive |
|---|---|---|
| Training set | Multimodal ocular fundus images and corresponding reports collected from multiple screening sites in China. | |
| Internal Validation set | Records separated from the training set. | |
| External Test set | Multimodal ocular fundus images and corresponding reports collected from multi-centers in China and around the world. Receives: Diagnostic Test: Various modalities of ocular fundus imaging |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve of the deep learning system
The investigators will calculate the area under the receiver operating characteristic curve of the deep learning system and compare this index with human ophthalmologists.
Measured: Baseline
Secondary outcomes (2)
Intersection-Over-Union of the models' explanation accuracy
The investigators will calculate the Intersection-Over-Union (IOU) (or Jaccard similarity) between the lesion-image attention mapping regions and ground truth regions of the deep learning system.
Measured: Baseline
Sensitivity and Specificity of the deep learning system
The investigators will calculate the sensitivity and specificity of the deep learning system.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Nov 2022
- Study startJan 2011
- Primary completionDec 2023 (estimated)
- Study completionJul 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ophthalmological DisorderStudies 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
- 15,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yingfeng Zheng, Sun Yat-sen University
- Investigators
- Yingfeng Zheng, M.D. Ph.D, Principal investigator, Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity,Guangzhou, Guangdong, China, 510060
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05622565
- Sponsor's protocol ID
- 2021KYPJ164
- Status last verified
- Jul 2023
- First submitted
- 15 Nov 2022
- Last updated
- 11 Jul 2023
- Results posted
- No
- Data monitoring committee
- 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 11 Jul 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Explainable Multimodal Deep Neural Networks for Identifying Ocular Fundus Diseases and Report Generation
- ClinicalTrials.gov ID
- NCT05622565
- Acronym / sponsor ID
- 2021KYPJ164
- Conditions
- Ophthalmological Disorder, Image, Body
- Keywords
- Retinal disease, Choroidal disease, Deep learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 15,000 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- Jan 2011 (actual)
- Primary completion
- Dec 2023 (estimated)
- Study completion
- Jul 2024 (estimated)
- First posted
- 18 Nov 2022 (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.
To establish a deep learning system of various ocular fundus disease analytics based on the results of multimodal examination images. The system can analyze multimodal ocular fundus images, make diagnoses and generate corresponding reports.
Detailed description
The ocular fundus is the only part of the human body that can directly see the blood vessel microcirculation and nerve tissue. Through various imaging tests, including Color Fundus Photograph (CFP), Optical Coherence Tomography (OCT), Fluorescein Fundus Angiography (FFA) and Indocyanine Green Angiography (ICGA), etc., it is possible to statically overview or dynamically observe the retina and choroid, the condition of blood vessels and nerves, and comprehensive diagnosis of the disease. The screening, interpreting and accurate diagnosis of ocular fundus diseases are crucial for disease prevention, control and precise treatment. However, due to the variety of fundus examination methods, and the complexity and professionalism of the examination, there is a lack of fundus specialists who have sufficient clinical experience and knowledge to interpret fundus examinations. With the continuous development of artificial intelligence (AI) in diagnosing fundus diseases, various modalities of imaging examination methods are gradually applied to the development of fundus disease diagnosis systems. Moreover, medical images often come with corresponding reports, which are mostly generated by clinicians' or radiologists' experience.
Here, we are establishing a fundus disease diagnosis and report-generating system based on cross-modal ocular fundus imaging examinations, and fundus lesions were visualized at the same time. Multi-center data verification will also be conducted. The results of the research will assist in fundus lesions diagnosis and imaging reports generation. We hope this could popularize more complex fundus imaging examination methods to society, and help improve the early diagnosis and treatment of fundus lesions that cause blindness.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training set | – | –Multimodal ocular fundus images and corresponding reports collected from multiple screening sites in China. |
| Internal Validation set | – | –Records separated from the training set. |
| External Test set | – | Diagnostic Test: Various modalities of ocular fundus imagingMultimodal ocular fundus images and corresponding reports collected from multi-centers in China and around the world. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Ocular fundus images were collected from different health care institutes all over China and from other countries.
Key inclusion criteria (1 listed)
- The quality of multimodal ocular fundus disease examination images and corresponding reports should be clinically acceptable.
Key exclusion criteria (2 listed)
- Reports with key information missing.
- Images with severe image resolution reductions, blur or artifacts were excluded from further analysis.
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 |
|---|---|---|
| Various modalities of ocular fundus imagingThrough various modalities of ocular fundus imaging, combining with clinical data and the experience of clinicians to diagnose different fundus diseases. | Diagnostic test | External Test set |
Primary outcome
Area under the receiver operating characteristic curve of the deep learning system
Time frame: Baseline
The investigators will calculate the area under the receiver operating characteristic curve of the deep learning system and compare this index with human ophthalmologists.
2 secondary outcomes
Intersection-Over-Union of the models' explanation accuracy
Time frame: Baseline
Sensitivity and Specificity of the deep learning system
Time frame: Baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jul 2023.
- Individual participant dataSharing planned: No
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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.
Yingfeng Zheng, M.D. Ph.D principal investigator
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity,Guangzhou, Guangdong, China, 510060
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 Ophthalmological Disorder and Image, Body, 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 1 inclusion and 2 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 Jul 2023. 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?
Contact Yingfeng Zheng, M.D. Ph.D (+8613922286455, zhyfeng@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the receiver operating characteristic curve of the deep learning system (time frame: Baseline). 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 11 Jul 2023.