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

NCT05208931 | Completed DMC

• 1 other identifier: 1
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

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.

Conditions

Neovascular Age-related Macular Degeneration

Keywords

Machine learning; Artificial intelligence; anti-VEGF therapy; AMD

Outcome Measures

Primary Outcomes (1)

• Prediction algorithm

  Neural network classifier

  1.09.2022

Study Arms (3)

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

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

разрыв

group in which neuroepithelial detachment rupture was observed after anti-vascular endothelial growth factor therapy

Procedure: Anti-vascular endothelial growth factor therapy

Interventions

Anti-vascular endothelial growth factor therapy PROCEDURE

0.05 ml anti-VEGF, intravitreal, monthly

Also known as: anti-VEGF

adhesion; no adhesion; разрыв

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients with retinal pigment epithelium detachments with age-related neovascular macular degeneration

You may qualify if:

• Linear B - scan through the macular area with the longest detachment
• Other pathologies

You may not qualify if:

• 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.

Sponsors & Collaborators

• The S.N. Fyodorov Eye Microsurgery State Institution: lead

Study Sites (1)

The S.N. Fyodorov Eye Microsurgery State Institution

Krasnodar, 350012, Russia

Location

Related Publications (6)

• 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.

  PMID: 29454659 BACKGROUND

• 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.

  PMID: 29127485 BACKGROUND

• 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.

  PMID: 31047298 BACKGROUND

• 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.

  PMID: 28658477 BACKGROUND

• 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.

  PMID: 29971444 BACKGROUND

• 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.

  BACKGROUND

Study Officials

• Viktoria Myasnikova, D.Med.Sc.

  Deputy Director for Research

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Deputy Director for Research

Study Record Dates

• First Submitted: January 5, 2022
• First Posted: January 26, 2022
• Study Start: November 1, 2021
• Primary Completion: September 1, 2022
• Study Completion: September 1, 2022
• Last Updated: April 13, 2023

Record last verified: 2023-04

Locations

RU (1)