Development of an Optimal Algorithm for the Management of Patients With Retinal Pigment Epithelium Detachment in Neovascular Age-related Macular Degeneration Using Artificial Intelligence
Study Details
Study Description
Brief Summary
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
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 Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| adhesion the group in which the adhesion of neuroepithelial detachment was observed after Anti-vascular endothelial growth factor therapy |
Procedure: Anti-vascular endothelial growth factor therapy
0.05 ml anti-VEGF, intravitreal, monthly
Other Names:
|
| no adhesion group in which there was no adherence of neuroepithelial detachment after Anti-vascular endothelial growth factor therapy |
Procedure: Anti-vascular endothelial growth factor therapy
0.05 ml anti-VEGF, intravitreal, monthly
Other Names:
|
| разрыв group in which neuroepithelial detachment rupture was observed after anti-vascular endothelial growth factor therapy |
Procedure: Anti-vascular endothelial growth factor therapy
0.05 ml anti-VEGF, intravitreal, monthly
Other Names:
|
Outcome Measures
Primary Outcome Measures
- Prediction algorithm [1.09.2022]
Neural network classifier
Eligibility Criteria
Criteria
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.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | The S.N. Fyodorov Eye Microsurgery State Institution | Krasnodar | Russian Federation | 350012 |
Sponsors and Collaborators
- The S.N. Fyodorov Eye Microsurgery State Institution
Investigators
- Study Director: Viktoria Myasnikova, D.Med.Sc., Deputy Director for Research
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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.
- Prahs P, Radeck V, Mayer C, Cvetkov Y, Cvetkova N, Helbig H, Märker 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.
- Rohm M, Tresp V, Müller 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.
- 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.
- 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.
- 1