Evaluation of The Performance of Retinow AI Software

NCT06645964 | Completed

• 2 other identifiers: E1-23-4281Sponsor
• Study Type: observational
• Target: 915
• Locations: 1 country
• Sites: 1

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.

Conditions

Diabetic Retinopathy; Glaucoma; Macular Degeneration

Keywords

Diabetic retinopathy; glaucoma; macular degeneration; artificial intelligence; retinow ai software; diagnosis of retinal disease; retinal disease

Outcome Measures

Primary Outcomes (3)

• Sensitivity

  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).

  1 visit (1 day)

• Specificity

  The ability of the software to correctly identify healthy individuals. High specificity minimizes false positive results. (True negative rate).

  1 visit (1 day)

• Accuracy

  It is defined as the percentage of correct results in all diagnoses of the software.

  1 visit (1 day)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This clinical study aims to evaluate the accuracy and reliability of Retinow AI software in diagnosing diabetic retinopathy, glaucoma and macular degeneration diseases. The study population includes individuals over the age of 18 who are suspected or diagnosed with these diseases, as well as healthy individuals with no pathological findings in their retinas.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Retinow Health Technologies and R&D Industry Joint Stock Company: lead

Study Sites (1)

Ankara Bilkent Şehir Hastanesi

Ankara, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Diabetic Retinopathy; Glaucoma; Macular Degeneration; Retinal Diseases

Condition Hierarchy (Ancestors)

Eye Diseases; Diabetic Angiopathies; Vascular Diseases; Cardiovascular Diseases; Diabetes Complications; Diabetes Mellitus; Endocrine System Diseases; Ocular Hypertension; Retinal Degeneration

Study Officials

• Tuğba Haklı Piroğlu, Master

  Authorized Representative

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 9, 2024
• First Posted: October 17, 2024
• Study Start: June 1, 2023
• Primary Completion: October 1, 2023
• Study Completion: October 1, 2023
• Last Updated: October 17, 2024

Record last verified: 2024-10

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)