Ignite Creation Date:
2024-10-26 @ 3:43 PM
Last Modification Date:
2024-10-26 @ 3:43 PM
Study NCT ID:
NCT06645964
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-10-09
Brief Title:
Evaluation of The Performance of Retinow AI Software
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Analysis of Diabetic Retinopathy Glaucoma and Macular Degeneration Diagnosis Via Digital Fundus Images With Artificial Intelligence
Status:
COMPLETED
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 AIs 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 softwares 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
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 AIs 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 softwares 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 softwares ability to diagnose diabetic retinopathy glaucoma and macular degeneration diseases with high accuracy through fundus photographs The softwares 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 AIs 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 141552020 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 conditionsMethods 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
Primary Objective
The primary objective is to evaluate the Retinow AI softwares ability to diagnose diabetic retinopathy glaucoma and macular degeneration diseases with high accuracy through fundus photographs The softwares 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 AIs 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 141552020 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 conditionsMethods 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None