An observational study in Diabetic Retinopathy, Retinal Vein Occlusion and Retinal Artery Occlusion, sponsored by Visionary Intelligence Ltd.. Completed at 5 sites in China. Open to participants aged 18 Years to 75 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2021-12-30.
Sponsored by Visionary Intelligence Ltd. · 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.
As a prospective clinical trial, This study enjoys multicentric, blind film reading, self-control and superiority test design. In total, 1,500 retinal fundus images from 750 individuals in need of fundus examination (one image for every single eye) were selected. Then a test group, along with a control group was set up in our study. For the test group, ophthalmologists read images with the aid of the assistant software(MCS). In contrast, the same work in the control group was finished by ophthalmologists independently. Meanwhile, the gold standard were obtained from the cooperation of senior ophthalmologists. Diagnoses of both groups were compared with those of the gold standard, thus the investigators could evaluated the safety and effectiveness of this assistant software in diagnosis.
The primary endpoint of this study is the superiority of the consistency rate of the test group. A diagnosis for an image is consistent if it gives the same negative result as the reference standard, or reveals any one condition indicated by the reference standard. The consistency rate is the rate of consistent diagnoses for all the involved images. One control group is designed, where each doctor reads and diagnoses, and give at most 3 possible conditions for each image. In the test group, doctors do the same thing with the help of this software. The investigators in the test group and control group are the same and they are chosen from ophthalmologists with 1\~3 years experience. The reference standard of each fundus image is collaboratively given by retinal specialists/fellows from 5 centers. The investigator of XieHe center is the arbitrator if full consensus cannot be reached for any image during the building of reference standard.
Any patients who meet the eligibility criteria.
Exclusion Criteria:
ophthalmologists read images applying the assistant software
Diagnostic Test: Software assisted imaging diagnosis
ophthalmologists read images independently
In the test group, diagnoses are given with the help of the software.
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.
Time frame: through study completion, an average of 1 year
sensitivity and specificity of software's diagnoses for each diseases
sensitivity and specificity of software's diagnoses for each diseases
Time frame: through study completion, an average of 1 year
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
Time frame: through study completion, an average of 1 year
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.
Time frame: through study completion, an average of 1 year
This study is completed, as verified in Dec 2021. You cannot join it, but the record below documents what was studied.
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