Evaluation of The Performance of Retinow AI Software
- Conditions
- Diabetic RetinopathyGlaucomaMacular Degeneration
- Registration Number
- NCT06645964
- Lead Sponsor
- Retinow Health Technologies and R&D Industry Joint Stock Company
- Brief Summary
Eye diseases are a major public health problem worldwide and one of the main causes of vision loss. Diseases such as diabetic retinopathy, glaucoma and macular degeneration in particular can lead to serious vision loss and negatively affect quality of life. Early diagnosis of these diseases, determination of appropriate treatment methods and protection of patients' quality of life are of great importance.
In recent years, artificial intelligence (AI) technologies have offered great opportunities for disease diagnosis and management in the medical field. Artificial intelligence algorithms developed for retinal image analysis have become an effective tool in the early diagnosis of eye diseases such as diabetic retinopathy, glaucoma and macular degeneration. Ophthalmic imaging and scanning systems supported by AI technology facilitate the diagnosis of these diseases and contribute to the treatment processes.
Artificial intelligence can provide an effective solution for automatic diagnosis of this disease and prediction of disease progression. Retinow AI was developed to accelerate early diagnosis of these three important eye diseases (diabetic retinopathy, glaucoma, macular degeneration), increase access and reduce costs. This software aims to provide a solution to the shortage of ophthalmologists and the limitations of existing methods. Retinow AI's ability to diagnose these diseases with high sensitivity and accuracy through fundus photographs is being evaluated within the scope of clinical research. According to the hypothesis, the software's accuracy rate can reach 90%, thus speeding up clinical processes and reducing the workload of healthcare personnel. In addition, it is planned to be used as an effective screening tool in regions where ophthalmologists are insufficient.
- Detailed Description
The main purpose of this clinical study is to evaluate the effectiveness and reliability of Retinow AI software in the diagnosis of common eye diseases such as diabetic retinopathy, glaucoma and macular degeneration. Retinow AI is a cloud-based artificial intelligence software that aims to detect diabetic retinopathy, glaucoma and macular degeneration diseases through fundus photographs. The software stands out with its ability to detect disease symptoms at an early stage and accelerate the diagnosis process. In addition, it is claimed that this software, which has reached a 90% accuracy rate during pre-clinical validation studies, can achieve similar results to the diagnostic accuracy of specialist physicians. This study examines the usability of Retinow AI software by both specialist and non-specialist physicians and its potential to save time in diagnostic processes. It is anticipated that the software can improve patient management, reduce costs and increase the efficiency of general healthcare services by accelerating the diagnosis of eye diseases. Certain eligibility criteria have been defined for the subjects and users to be examined within the scope of the study. These criteria are designed to reliably evaluate the performance of Retinow AI software. Retinow AI is designed for use by healthcare providers. The user must have sufficient understanding of the language in which the user manual was prepared.
Primary Objective:
The primary objective is to evaluate the Retinow AI software's ability to diagnose diabetic retinopathy, glaucoma, and macular degeneration diseases with high accuracy through fundus photographs. The software's performance was compared with diagnoses made by specialist physicians, and accuracy, sensitivity, and specificity metrics were measured. The hypothesis that Retinow AI can achieve 90% accuracy was tested. In this context, the Retinow AI's ability to consistently identify the same disease symptoms in different fundus images was evaluated by analyzing false positive and false negative results.
Primary Hypothesis:
It is hypothesized that Retinow AI software can diagnose eye diseases such as diabetic retinopathy, glaucoma, and macular degeneration at an early stage through fundus photographs with 90% accuracy. It is anticipated that the software can achieve similar sensitivity and specificity rates to the evaluations of specialist physicians in the diagnosis of these diseases.
This clinical study is a single-center, observational, prospective, and cross-sectional study conducted at Ankara Bilkent City Hospital to determine the primary endpoints of sensitivity, specificity, and accuracy of the Retinow AI device for diabetic retinopathy, glaucoma, and macular degeneration.
The clinical trial, clinical trial plan, clinical trial results report were conducted in accordance with the ethical principles of the Declaration of Helsinki, the principles of SS-EN ISO 14155:2020, and the current national and international regulations governing this clinical trial. A signed Declaration of Helsinki was obtained from all participants in the clinical trial.
The study included healthy participants who did not have diabetic retinopathy, glaucoma, macular degeneration, or any eye disease (no pathological findings in the retina). The study population consisted of 940 participants aged 18 years and older who applied to an ophthalmologist for an eye examination to detect diabetic retinopathy, macular degeneration, and glaucoma. 25 of the participants were excluded from the study because they did not meet the inclusion criteria. The study included 153 participants for Diabetic Retinopathy, 153 for Glaucoma, and 153 for Macular Degeneration. A total of 456 participants, 152 from each disease group, were healthy participants with no pathological findings in their retinas. A total of 915 participants who completed all procedures were included in this study. Pregnant women were not included in the study. Pregnancy does not have a long-term effect on diabetic retinopathy. Even if retinopathy progresses during pregnancy, it regresses after delivery. Retinow AI is not intended for use in patients with gestational diabetes because diabetic retinopathy can progress very rapidly during pregnancy. Retinow AI is not intended to evaluate rapidly progressing diabetic retinopathy. The software is designed to detect diabetic retinopathy, glaucoma, and macular degeneration only and should not be used to detect other diseases or conditions.Methods and Tools Used in the Study:
* In order to diagnose diabetic retinopathy, glaucoma and macular degeneration, analyze the retina of healthy individuals and enable specialist physicians to diagnose diseases from fundus images, a Canon Europe BV brand color video 3CCD camera mounted on a Topcon TRC-NW6 nonmydriatic fundus camera (Topcon USA, Inc.) with a 45° field of view and centered on the fovea was used during routine eye examinations.
* Retinow AI software: Retinow software was used by retina specialists to evaluate fundus images and compare results for clinical validation of the Retinow AI device. Retinow AI is an artificial intelligence-based software that diagnoses eye diseases by analyzing fundus images. The analysis results of the software were periodically compared with the results of specialist ophthalmologists. Since the latest version of the Retinow AI software was used, no changes were made to the software throughout the clinical study.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 915
- Patients with diabetic retinopathy, glaucoma or macular degeneration
- Volunteers with written informed consent form
- Volunteer must be 18 years or older
- Healthy individuals without retinal disorders
- Volunteers who do not want to have fundus imaging
- Cases that do not comply with fundus photography for any reason
- Patients with conjunctival and corneal infections,
- People with hereditary or congenital retinal diseases,
- People with cataracts,
- People with uveitis,
- Patients with permanent visual impairment in one or both eyes,
- Patients with correction of + 6D and above - 6D,
- Pregnant woman
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Sensitivity 1 visit (1 day) The ability of the software to correctly identify individuals with the disease. High sensitivity reduces the probability that the software will miss signs of the disease. (True positive rate).
Specificity 1 visit (1 day) The ability of the software to correctly identify healthy individuals. High specificity minimizes false positive results. (True negative rate).
Accuracy 1 visit (1 day) It is defined as the percentage of correct results in all diagnoses of the software.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Ankara Bilkent Şehir Hastanesi
🇹🇷Ankara, Turkey