Development of an Optimal Algorithm for the Management of Patients With Retinal Pigment Epithelium Detachment in Neovascular Age-related Macular Degeneration Using Artificial Intelligence

Neovascular Age-related Macular Degeneration Clinical Trial

Official title:

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.

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05208931
• Other study ID #: 1
• Status: Completed
• Start date: November 1, 2021
• Est. completion date: September 1, 2022

Study information

• Verified date: April 2023
• Source: The S.N. Fyodorov Eye Microsurgery State Institution
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 300
• Est. completion date: September 1, 2022
• Est. primary completion date: September 1, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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.

Related Conditions & MeSH terms

• Macular Degeneration
• Neovascular Age-related Macular Degeneration
• Wet Macular Degeneration

Intervention

Procedure:
• Anti-vascular endothelial growth factor therapy
0.05 ml anti-VEGF, intravitreal, monthly

Locations

Country	Name	City	State
Russian Federation	The S.N. Fyodorov Eye Microsurgery State Institution	Krasnodar

Sponsors (1)

Lead Sponsor	Collaborator
The S.N. Fyodorov Eye Microsurgery State Institution

Country where clinical trial is conducted

Russian Federation, 

References & Publications (6)

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. — View Citation

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, 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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Prediction algorithm	Neural network classifier	1.09.2022