Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT04718532 |
| Other study ID # |
?2019-428 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
January 1, 2016 |
| Est. completion date |
December 31, 2023 |
Study information
| Verified date |
January 2021 |
| Source |
Second Affiliated Hospital, School of Medicine, Zhejiang University |
| Contact |
Jin Kai, MD |
| Phone |
13646828461 |
| Email |
jinkai[@]zju.edu.cn |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational [Patient Registry]
|
Clinical 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 very important significance
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.
