Analysis of Diabetic Retinopathy, Glaucoma and Macular Degeneration Diagnosis Via Digital Fundus Images With Artificial Intelligence
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 915
- 试验地点
- 1
- 主要终点
- Sensitivity
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- •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
结局指标
主要结局
Sensitivity
时间窗: 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
时间窗: 1 visit (1 day)
The ability of the software to correctly identify healthy individuals. High specificity minimizes false positive results. (True negative rate).
Accuracy
时间窗: 1 visit (1 day)
It is defined as the percentage of correct results in all diagnoses of the software.
次要结局
未报告次要终点