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

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.

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05208931

Enrollment

300

Registered

2022-01-26

Start date

2021-11-01

Completion date

2022-09-01

Last updated

2023-04-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Neovascular Age-related Macular Degeneration

Keywords

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

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.

Interventions

PROCEDUREAnti-vascular endothelial growth factor therapy

0.05 ml anti-VEGF, intravitreal, monthly

Study design

Observational model

CASE_CONTROL

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

Healthy volunteers

Yes

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.

Design outcomes

Primary

Measure	Time frame	Description
Prediction algorithm	1.09.2022	Neural network classifier

Countries

Russia

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026