- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05208931
Development of an Optimal Algorithm for the Management of Patients With Retinal Pigment Epithelium Detachment in Neovascular Age-related Macular Degeneration Using Artificial Intelligence
April 11, 2023 updated by: Viktoria Myasnikova, The S.N. Fyodorov Eye Microsurgery State Institution
Development of an Algorithm for Predicting Anatomical and Functional the Results of Therapy With Angiogenesis Inhibitors in Patients With Retinal Pigment Epithelium Detachments in Neovascular Age-related Macular Degeneration, Based on Primary Optical Coherence Tomography of the Macular Zone and Clinical Data.
The study involves the development of an algorithm for predicting anatomical and functional results of therapy with angiogenesis inhibitors in patients with retinal pigment epithelium detachments in neovascular age-related macular degeneration, based on primary optical coherence tomography of the macular zone and clinical data.
Study Overview
Status
Completed
Conditions
Intervention / Treatment
Detailed Description
Patients were divided into 3 groups according to the results of therapy: adhesion of detachment, lack of adherence to detachment, rupture of detachment.
For these groups, OCT images of the macular zone with maximum detachment before therapy are selected.
These images, along with other clinical parameters, are input to the algorithm.
The result is one of the 3 treatment outcomes listed above.
The methods that will be used to develop the algorithm include methods for processing and transforming data, deep machine learning, metrics for calculating the accuracy of algorithms.
Study Type
Observational
Enrollment (Actual)
300
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
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Krasnodar, Russian Federation, 350012
- The S.N. Fyodorov Eye Microsurgery State Institution
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Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Yes
Sampling Method
Probability Sample
Study Population
Patients with retinal pigment epithelium detachments with age-related neovascular macular degeneration
Description
Inclusion criteria:
- Linear B - scan through the macular area with the longest detachment
- Other pathologies
Exclusion criteria:
- Images without detachment
- Images on which it is possible to diagnose the need for therapy only in the presence of additional factors not considered in the study.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
adhesion
the group in which the adhesion of neuroepithelial detachment was observed after Anti-vascular endothelial growth factor therapy
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0.05 ml anti-VEGF, intravitreal, monthly
Other Names:
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no adhesion
group in which there was no adherence of neuroepithelial detachment after Anti-vascular endothelial growth factor therapy
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0.05 ml anti-VEGF, intravitreal, monthly
Other Names:
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разрыв
group in which neuroepithelial detachment rupture was observed after anti-vascular endothelial growth factor therapy
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0.05 ml anti-VEGF, intravitreal, monthly
Other Names:
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Prediction algorithm
Time Frame: 1.09.2022
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Neural network classifier
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1.09.2022
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Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Investigators
- Study Director: Viktoria Myasnikova, D.Med.Sc., Deputy Director for Research
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- Rohm M, Tresp V, Muller M, Kern C, Manakov I, Weiss M, Sim DA, Priglinger S, Keane PA, Kortuem K. Predicting Visual Acuity by Using Machine Learning in Patients Treated for Neovascular Age-Related Macular Degeneration. Ophthalmology. 2018 Jul;125(7):1028-1036. doi: 10.1016/j.ophtha.2017.12.034. Epub 2018 Feb 14.
- Prahs P, Radeck V, Mayer C, Cvetkov Y, Cvetkova N, Helbig H, Marker D. OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. Graefes Arch Clin Exp Ophthalmol. 2018 Jan;256(1):91-98. doi: 10.1007/s00417-017-3839-y. Epub 2017 Nov 10.
- Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, Schlegl T, Langs G, Gerendas BS, Osborne A, Waldstein SM. Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration. Ophthalmol Retina. 2018 Jan;2(1):24-30. doi: 10.1016/j.oret.2017.03.015. Epub 2017 May 31.
- Bogunovic H, Montuoro A, Baratsits M, Karantonis MG, Waldstein SM, Schlanitz F, Schmidt-Erfurth U. Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging. Invest Ophthalmol Vis Sci. 2017 May 1;58(6):BIO141-BIO150. doi: 10.1167/iovs.17-21789.
- Schmidt-Erfurth U, Waldstein SM, Klimscha S, Sadeghipour A, Hu X, Gerendas BS, Osborne A, Bogunovic H. Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence. Invest Ophthalmol Vis Sci. 2018 Jul 2;59(8):3199-3208. doi: 10.1167/iovs.18-24106.
- Kozina, E. V., S. N. Sakhnov, V. V. Myasnikova, E. V. Bykova, and L. E. Aksenova. 2021. 'Modern Trends in Diagnostics and Prediction of Results of Anti-Vascular Endothelial Growth Factor Therapy of Pigment Epithelial Detachment in Neovascular Agerelated Macular Degeneration Using Deep Machine Learning Method (Literature Review)'. Acta Biomedica Scientifica 6 (6-1): 190-203. https://doi.org/10.29413/ABS.2021-6.6-1.22.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
November 1, 2021
Primary Completion (Actual)
September 1, 2022
Study Completion (Actual)
September 1, 2022
Study Registration Dates
First Submitted
January 5, 2022
First Submitted That Met QC Criteria
January 12, 2022
First Posted (Actual)
January 26, 2022
Study Record Updates
Last Update Posted (Actual)
April 13, 2023
Last Update Submitted That Met QC Criteria
April 11, 2023
Last Verified
April 1, 2023
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 1
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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