A New Technique For Retinal Disease Treatment

January 18, 2021 updated by: Second Affiliated Hospital, School of Medicine, Zhejiang University

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

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Detailed Description

  1. 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.
  2. 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.
  3. 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.
  4. 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 Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Anticipated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact

The name and contact information for the person who can answer enrollment questions for a clinical study. Each location where the study is being conducted may also have a specific contact, who may be better able to answer those questions.

Study Locations

  • China
    • Zhejiang
      • Hanzhou, Zhejiang, China
        • Recruiting
        • The Second Affiliated Hospital of Zhejiang University
        • Contact:
          • Jin Kai, MD
          • Phone Number: 13646828461

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

patients undergo retinal exmination at eye center in the Second Affiliated Hospital of Zhejiang University

Description

Inclusion Criteria:

  • patients with retinal diseases

Exclusion Criteria:

  • patients with other disease affect retinal exmination

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
patients
patients with retinal diseases

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
artificial intelligence
Time Frame: 2016.01-2023.12
using data to develop deep learning models
2016.01-2023.12

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Second Affiliated Hospital, School of Medicine, Zhejiang University

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • Principal Investigator: Jin Kai, MD, Zhejiang University
  • Principal Investigator: Xu Yufeng, MD, Zhejiang University
  • Principal Investigator: Lou Lixia, MD, Zhejiang University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
January 1, 2016

Primary Completion (Anticipated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
January 1, 2023

Study Completion (Anticipated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
December 31, 2023

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
January 18, 2021

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
January 18, 2021

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
January 22, 2021

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
January 22, 2021

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
January 18, 2021

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
January 1, 2021

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • 研2019-428

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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