Retina DisorderNCT04718532
A New Technique For Retinal Disease Treatment
An observational study of Retina Disorder, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 1 site in China.
Trial summary
With the advent of the era of precision medicine, based on FFA image deep learning to identify the area of fundus lesions, registration of fundus images, according to the severity of fundus diseases to design the optimal laser energy and path, the accurate treatment of fundus diseases has urgent clinical needs and… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Retina Disorder
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2016to 31 Dec 2023
- How long will I take part?
- 4 Years
- 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: patients undergo retinal exmination at eye center in the Second Affiliated Hospital of Zhejiang University
Who may be able to join
- patients with retinal diseases
Who cannot join
- patients with other disease affect retinal exmination
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
The Second Affiliated Hospital of Zhejiang University
Hanzhou, Zhejiang
Jin Kai, MD, 13646828461
About this study
Official title: Research of Intelligent Diagnosis and Automatic Lesion Tracking for Precise Treatment of Retinal Diseases Based on Deep-learning
With the advent of the era of precision medicine, based on FFA image deep learning to identify the area of fundus lesions, registration of fundus images, according to the severity of fundus diseases to design the optimal laser energy and path, the accurate treatment of fundus diseases has urgent clinical needs and very important significance
Read the detailed description
- Structured DR Image Database Construction and accurate annotation: retrospective (from January 1, 2016 to the day of ethical review) and prospective (from the day of ethical review to December 31, 2023) collected FFA and other multimodal image data. Several ophthalmologists and senior experts of fundus diseases made diagnostic evaluation on each image of each patient and completed the accurate grading diagnosis of the data Finally, a structured Dr database was established step by step. This paper uses the theory of computer vision to quantify the quality distortion of FFA image, guides the computer to configure the existing image enhancement and noise reduction algorithms adaptively, and completes the preprocessing of fundus image data.
- Construction of Dr intelligent grading diagnosis system based on fundus image: firstly, the fundus image is used as the fundus data training database, and according to the international clinical Dr grading diagnosis standard, many doctors mark the fundus image accurately. International clinical Dr grading criteria: grade 0, no obvious retinal abnormalities; grade 1, only microangioma; grade 2, more severe than microangioma, but less severe than severe; grade 3, four quadrants, each quadrant has more than 20 retinal hemorrhage, more than two quadrants have definite venous beads, more than one quadrant has obvious Irma, no signs of proliferative retinopathy; grade 4, neovascularization, vitreous hemorrhage Volume blood, pre retinal hemorrhage. On the basis of Dr grading intelligent diagnosis standard, convolution neural network is constructed to train and grade fundus images. After repeating this process many times for each image in the training set of fundus images, the deep learning system learns how to classify all the data in the training set to accurately diagnose the fundus images.
- Convolution neural network construction for FFA image focus area: the convolution neural network of deep learning is composed of millions of parameters, which is used to train and perform given tasks. The output generated by each linear convolution operation is regularized by nonlinear activation function, combined with the dimensionality reduction of pooling layer and full connection layer, so that the optimization process of deep neural network not only overcomes the gradient dispersion, but also helps to generate features similar to the hierarchical perception mechanism of human neural cells to visual signals. The FFA image is used as the fundus data training database. Based on the accurate labeling of the lesion area (no perfusion area, microangioma area and leakage area), the FFA image needs to be treated for the intelligent recognition of the lesion area. In the training process, the parameters of the neural network are initially set to random values. Then, for each image, the results given by the function are compared with the known results of the training set to optimize the parameters of the function. After repeating this process many times for each image in the training data set, the deep learning system learned how to classify all the data in the training set to accurately predict the Dr lesions on FFA images.
- Construction of intelligent fundus laser navigation model based on FFA image and fundus image registration: the Dr lesion intelligent recognition system on the above FFA image accurately identifies the areas that need fundus laser treatment, helps doctors determine the lesions that need to be treated, and based on the image matching of machine learning, provides the registration image of fundus image and FFA combination, which is set according to the location and size information of the lesion area According to the matching retinal diameter and the arrangement of different laser spots, the personalized laser treatment scheme is formulated, and the intelligent fundus laser treatment guidance model is constructed.
- Conditions
- Retina Disorder
- Medical subject headings
- Retinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| patients | patients with retinal diseases |
What is being measured
The primary outcome is the main question the study is designed to answer.
artificial intelligence
using data to develop deep learning models
Measured: 2016.01-2023.12
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jan 2021
- Study start1 Jan 2016
- Primary completion1 Jan 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Retina DisorderStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital, Zhejiang University, School of MedicineThe 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
- Case crossover
- Time perspective
- Cross-sectional
- Enrolment
- 2,000 participants (estimated)
- Target duration
- 4 Years
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Jin Kai, MD, Principal investigator, Zhejiang UniversityXu Yufeng, MD, Principal investigator, Zhejiang UniversityLou Lixia, MD, Principal investigator, Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04718532
- Sponsor's protocol ID
- 研2019-428
- Status last verified
- Jan 2021
- First submitted
- 18 Jan 2021
- Last updated
- 22 Jan 2021
- 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 22 Jan 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research of Intelligent Diagnosis and Automatic Lesion Tracking for Precise Treatment of Retinal Diseases Based on Deep-learning
- ClinicalTrials.gov ID
- NCT04718532
- Acronym / sponsor ID
- 研2019-428
- Conditions
- Retina Disorder
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Study start
- 1 Jan 2016 (actual)
- Primary completion
- 1 Jan 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 22 Jan 2021 (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.
With the advent of the era of precision medicine, based on FFA image deep learning to identify the area of fundus lesions, registration of fundus images, according to the severity of fundus diseases to design the optimal laser energy and path, the accurate treatment of fundus diseases has urgent clinical needs and very important significance
Detailed description
- Structured DR Image Database Construction and accurate annotation: retrospective (from January 1, 2016 to the day of ethical review) and prospective (from the day of ethical review to December 31, 2023) collected FFA and other multimodal image data. Several ophthalmologists and senior experts of fundus diseases made diagnostic evaluation on each image of each patient and completed the accurate grading diagnosis of the data Finally, a structured Dr database was established step by step. This paper uses the theory of computer vision to quantify the quality distortion of FFA image, guides the computer to configure the existing image enhancement and noise reduction algorithms adaptively, and completes the preprocessing of fundus image data.
- Construction of Dr intelligent grading diagnosis system based on fundus image: firstly, the fundus image is used as the fundus data training database, and according to the international clinical Dr grading diagnosis standard, many doctors mark the fundus image accurately. International clinical Dr grading criteria: grade 0, no obvious retinal abnormalities; grade 1, only microangioma; grade 2, more severe than microangioma, but less severe than severe; grade 3, four quadrants, each quadrant has more than 20 retinal hemorrhage, more than two quadrants have definite venous beads, more than one quadrant has obvious Irma, no signs of proliferative retinopathy; grade 4, neovascularization, vitreous hemorrhage Volume blood, pre retinal hemorrhage. On the basis of Dr grading intelligent diagnosis standard, convolution neural network is constructed to train and grade fundus images. After repeating this process many times for each image in the training set of fundus images, the deep learning system learns how to classify all the data in the training set to accurately diagnose the fundus images.
- Convolution neural network construction for FFA image focus area: the convolution neural network of deep learning is composed of millions of parameters, which is used to train and perform given tasks. The output generated by each linear convolution operation is regularized by nonlinear activation function, combined with the dimensionality reduction of pooling layer and full connection layer, so that the optimization process of deep neural network not only overcomes the gradient dispersion, but also helps to generate features similar to the hierarchical perception mechanism of human neural cells to visual signals. The FFA image is used as the fundus data training database. Based on the accurate labeling of the lesion area (no perfusion area, microangioma area and leakage area), the FFA image needs to be treated for the intelligent recognition of the lesion area. In the training process, the parameters of the neural network are initially set to random values. Then, for each image, the results given by the function are compared with the known results of the training set to optimize the parameters of the function. After repeating this process many times for each image in the training data set, the deep learning system learned how to classify all the data in the training set to accurately predict the Dr lesions on FFA images.
- Construction of intelligent fundus laser navigation model based on FFA image and fundus image registration: the Dr lesion intelligent recognition system on the above FFA image accurately identifies the areas that need fundus laser treatment, helps doctors determine the lesions that need to be treated, and based on the image matching of machine learning, provides the registration image of fundus image and FFA combination, which is set according to the location and size information of the lesion area According to the matching retinal diameter and the arrangement of different laser spots, the personalized laser treatment scheme is formulated, and the intelligent fundus laser treatment guidance model is constructed.
Study design
- Observational model
- Case crossover
- Time perspective
- Cross-sectional
- Target follow-up
- 4 Years
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| patients | – | –patients with retinal diseases |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- patients undergo retinal exmination at eye center in the Second Affiliated Hospital of Zhejiang University
Key inclusion criteria (1 listed)
- patients with retinal diseases
Key exclusion criteria (1 listed)
- patients with other disease affect retinal exmination
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
artificial intelligence
Time frame: 2016.01-2023.12
using data to develop deep learning models
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jan 2021.
- 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.
Jin Kai, MD principal investigator
Zhejiang University
Xu Yufeng, MD principal investigator
Zhejiang University
Lou Lixia, MD principal investigator
Zhejiang University
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 Retina Disorder, run by Second Affiliated Hospital, Zhejiang University, School of Medicine 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 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 22 Jan 2021. 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 Jin Kai, MD (13646828461, jinkai@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
artificial intelligence (time frame: 2016.01-2023.12). 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 22 Jan 2021; AzTrials last refreshed its copy on 1 Nov 2026.