Screening and evaluation of diabetic retinopathy via a deep learning network model Eye Diseases
Conditions
Interventions
The intervention conditions in this study will involve the screening and evaluation of diabetic retinopathy in patients using a deep-learning network model. Participants, who will be diabetic individu
Sponsors
First People's Hospital of Linping District
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: 1. Aged 18 years old and older 2. Diagnosed diabetes, regardless of sex, race, or type of diabetes 3. Able to understand and agree to the study agreement and be willing to sign an informed consent form 4. Able to undergo an eye exam, including vision tests, fundus photography and optical coherence tomography 5. Patients should have a complete medical history, especially regarding the treatment and control of diabetes 6. Agree to regular follow-up visits during the study to monitor their condition and evaluate the screening effectiveness of the deep learning model
Exclusion criteria
Exclusion criteria: 1. Aged under 18 years old 2. Serious eye conditions (such as glaucoma, and macular degeneration) or other eye conditions that would interfere with retinal image analysis 3. Pregnancy or planning to become pregnant during the study period due to hormonal changes that may affect retinal conditions 4. Diabetes-related complications (such as diabetic nephropathy, and neuropathy) that are severe enough to affect study participation or results 5. Patients who are unable to understand or follow the study protocol 6. Patients who are unable to follow up regularly 7. Patients who have a serious systemic disease or unstable health condition (e.g., cardiovascular disease, and cancer) that could affect the study process or outcome 8. Patients who have participated in other clinical trials in the past 6 months, or are receiving other treatments that could affect the results of this study
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The effectiveness of the deep-learning network models in the early detection of diabetic retinopathy measured using the analysis of a large number of patient image data at one timepoint | — |
Secondary
| Measure | Time frame |
|---|---|
| The applicability and stability of the deep learning network model in different diabetes courses and complication states examined using data collected on the usefulness of the model in clinical operations, such as image processing speed, ease of operation, and impact on the training needs of medical personnel at one timepoint | — |
Countries
China
Contacts
Public ContactLi Yao
Outcome results
None listed