Diabetic Retinopathy Clinical Trial
Official title:
A Prospective, Multicenter, Blinded Reading, Self Controlled, Superiority Priority Clinical Trial of Assisted Fundus Image Diagnosis Software for the Diagnosis of Multiple Eye Fundus Diseases
NCT number | NCT04723160 |
Other study ID # | BLG-20200101 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | August 10, 2020 |
Est. completion date | May 30, 2021 |
Verified date | December 2021 |
Source | Visionary Intelligence Ltd. |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
Blindness can be caused by many ocular diseases, such as diabetic retinopathy, retinal vein occlusion, age-related macular degeneration, pathologic myopia and glaucoma. Without timely diagnosis and adequate medical intervention, the visual impairment can become a great burden on individuals as well as the society. It is estimated that China has 110 million patients under the attack of diabetes, 180 million patients with hypertension, 120 million patients suffering from high myopia and 200 million people over 60 years old, which suggest a huge population at the risk of blindness. Despite of this crisis in public health, our society has no more than 3,000 ophthalmologists majoring in fundus oculi disease currently. As most of them assembling in metropolitan cities, health system in this field is frail in primary hospitals. Owing to this unreasonable distribution of medical resources, providing medical service to hundreds of millions of potential patients threatened with blindness is almost impossible. To solve this problem, this software (MCS) was developed as a computer-aided diagnosis to help junior ophthalmologists to detect 13 major retina diseases from color fundus photographs. This study has been designed to validate the safety and efficiency of this device.
Status | Completed |
Enrollment | 748 |
Est. completion date | May 30, 2021 |
Est. primary completion date | March 10, 2021 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years to 75 Years |
Eligibility | Inclusion Criteria: - Age between 18 and 75. - Anyone need to take fundus photograph in clinical. - Understand the study and volunteer to sign the informed consent. - For fundus images of participants, the optic disc, fovea, the upper and lower vessel bow should be included in the fundus field. Exclusion Criteria: - Participants has any eye that cannot take fundus photos. - Participants have joined or is participating in other clinical trial within one month. - Participants who have any other issue that cannot be enrolled. - Participants with cloudy refractive media that cannot take fundus photos or get clouding fundus photos. - Participants with low quality fundus photos like incompetent vision field, overexposed/underexposed, out of focus, too many shadow or dirties and so on. |
Country | Name | City | State |
---|---|---|---|
China | Peking Union Medical College Hospital, Chinese Academy of Medical Sciences | BeiJing | Beijing |
China | West China Hospital of Sichuan University | Chengdu | Sichuan |
China | The Second Hospital of Hebei Medical University | Shijia Zhuang | Hebei |
China | Tianjin Medical University Eye Hospital | Tianjin | Tianjin |
China | Eye Hospital, WMU Zhejiang Eye Hospital | Wenzhou | Zhejiang |
Lead Sponsor | Collaborator |
---|---|
Visionary Intelligence Ltd. | Beijing Municipal Science & Technology Commission, Peking University First Hospital |
China,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | consistent rate of diagnoses | Formula for calculation: consistent rate of diagnoses=number of images with consistent diagnosis/ total number of images × 100%.
Method: the diagnoses from the test group and the control group were compared with diagnoses from the gold standard. For each image, if one or more diagnoses were consistent with those of the gold standard, which means at least one label existed in the intersection of diagnoses from the test group(or the control group)and those from the gold standard, it would be classified as "image with consistent diagnosis". Otherwise, it would be classified as "image without consistent diagnosis". After above-mentioned steps, the investigators had obtained the number of images with consistent diagnosis in each group. As images with 1-2 labels account for the majority in actual work, the investigators stipulated that each image in both groups could be marked with 3 labels at most in case of invalid improvement in consistent rate owing to multiple selections. |
through study completion, an average of 1 year | |
Secondary | sensitivity and specificity of software's diagnoses for each diseases | sensitivity and specificity of software's diagnoses for each diseases | through study completion, an average of 1 year | |
Secondary | PPV and NPV of software's diagnoses for each diseases | PPV(Positive Predictive Value) and NPV(Negative Predictive Value) of software's diagnoses for each diseases | through study completion, an average of 1 year | |
Secondary | full coincidence rate of software's diagnoses | The full consistency rate is the rate of fully consistent diagnoses in the set. A diagnosis is fully consistent it is exactly the same as the reference standard. | through study completion, an average of 1 year |
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