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
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.
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Neovascular Age-related Macular Degeneration
Keywords: Machine learning
Artificial intelligence
anti-VEGF therapy
AMD
Lead sponsor: The S.N. Fyodorov Eye Microsurgery State Institution
Sponsor class: OTHER_GOV
First posted: 2022-01-26
Enrollment: 300
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with retinal pigment epithelium detachments with age-related neovascular macular degeneration

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.

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.

Primary outcomes:
measure: Prediction algorithm
timeFrame: 1.09.2022
Secondary outcomes:
Other outcomes:
Locations:
city: Krasnodar
country: Russian Federation
facility: The S.N. Fyodorov Eye Microsurgery State Institution
Study officials:
affiliation: Deputy Director for Research
name: Viktoria Myasnikova, D.Med.Sc.
role: STUDY_DIRECTOR
References:
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.
citation: 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.
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.
citation: 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.
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.
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.