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 Overview
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Brief Summary
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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
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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.

Official Title
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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.

Conditions
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Neovascular Age-related Macular Degeneration

Intervention / Treatment
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* Procedure: Anti-vascular endothelial growth factor therapy


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| adhesion<br>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<br>* 0.05 ml anti-VEGF, intravitreal, monthly<br>* Other names: anti-VEGF;|
| no adhesion<br>group in which there was no adherence of neuroepithelial detachment after Anti-vascular endothelial growth factor therapy | Procedure: Anti-vascular endothelial growth factor therapy<br>* 0.05 ml anti-VEGF, intravitreal, monthly<br>* Other names: anti-VEGF;|
| разрыв<br>group in which neuroepithelial detachment rupture was observed after anti-vascular endothelial growth factor therapy | Procedure: Anti-vascular endothelial growth factor therapy<br>* 0.05 ml anti-VEGF, intravitreal, monthly<br>* Other names: anti-VEGF;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Prediction algorithm | Neural network classifier | 1.09.2022 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Machine learning, Artificial intelligence, anti-VEGF therapy, AMD