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

NCT ID: NCT05208931

Last Updated: 2023-04-13

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 300 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-11-01
• Study Completion Date: 2022-09-01

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.

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.

Conditions

Neovascular Age-related Macular Degeneration

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: RETROSPECTIVE

Study Groups

adhesion

the group in which the adhesion of neuroepithelial detachment was observed after Anti-vascular endothelial growth factor therapy

Anti-vascular endothelial growth factor therapy

Intervention Type: PROCEDURE

0.05 ml anti-VEGF, intravitreal, monthly

no adhesion

group in which there was no adherence of neuroepithelial detachment after Anti-vascular endothelial growth factor therapy

Anti-vascular endothelial growth factor therapy

Intervention Type: PROCEDURE

0.05 ml anti-VEGF, intravitreal, monthly

разрыв

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

Anti-vascular endothelial growth factor therapy

Intervention Type: PROCEDURE

0.05 ml anti-VEGF, intravitreal, monthly

Interventions

Anti-vascular endothelial growth factor therapy

0.05 ml anti-VEGF, intravitreal, monthly

Intervention Type: PROCEDURE

Other Intervention Names

anti-VEGF

Eligibility 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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

The S.N. Fyodorov Eye Microsurgery State Institution

OTHER_GOV

Sponsor Role: lead

Responsible Party

Viktoria Myasnikova

Deputy Director for Research

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Viktoria Myasnikova, D.Med.Sc.

Role: STUDY_DIRECTOR

Deputy Director for Research

Locations

The S.N. Fyodorov Eye Microsurgery State Institution

Krasnodar, , Russia

Countries

Russia

References

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.

Reference Type: BACKGROUND

PMID: 29454659 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 29127485 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 31047298 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 28658477 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 29971444 (View on PubMed)

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.

Reference Type: BACKGROUND

Other Identifiers

1

Identifier Type: -

Identifier Source: org_study_id