A deep machine learning model development for the biomarkers of the anatomical and functional anti-VEGF therapy outcome detection on retinal OCT images
Authors:   The S. Fyodorov Eye Microsurgery Federal State Institution, the Krasnodar Branch, Krasnodar, Russian Federation, Malyugin B.E. , Sakhnov S.N. ,   The S. Fyodorov Eye Microsurgery Federal State Institution, the Krasnodar Branch, Krasnodar, Russian Federation,   Kuban State Medical University, Kuban, Russian Federation, Axenova L.E. ,   The S. Fyodorov Eye Microsurgery Federal State Institution, the Krasnodar Branch, Krasnodar, Russian Federation,   LLC Predict Space, Novorossiysk, Russian Federation, Axenov K.D. ,   LLC Predict Space, Novorossiysk, Russian Federation, Kozina E.V. ,   The S. Fyodorov Eye Microsurgery Federal State Institution, the Krasnodar Branch, Krasnodar, Russian Federation, Vronskaya V.V. ,   The S. Fyodorov Eye Microsurgery Federal State Institution, the Krasnodar Branch, Krasnodar, Russian Federation, Myasnikova V.V. ,   The S. Fyodorov Eye Microsurgery Federal State Institution, the Krasnodar Branch, Krasnodar, Russian Federation,   LLC Predict Space, Novorossiysk, Russian Federation
Published: March 2023

Relevance. Nearly 200 million people worldwide suffer from agerelated macular degeneration (AMD), 10% of which is neovascular, the cause of severe vision loss for most patients. Vascular endothelial growth factor inhibitors (anti-VEGF therapy) make it possible to achieve regression of the neovascularization process and preserve vision. However, today it is a rather expensive method of treatment, which is accompanied by various complications. The neovascular form of agerelated macular degeneration is the most common cause of such a complication as rupture of the pigment epithelium. Predictors of this anatomical outcome, as well as predictors of functional outcome or final visual acuity, can be assessed using optical coherence tomography (OCT). To automatize the processes of identifying morphological structures in OCT images deep learning methods are used. Purpose. The aim of this work was to create an algorithm for the automated detection of the antiVEGF therapy outcome biomarkers in patients with n-AMD and PED on OCT images. Material and methods. We used a set of retrospective data in the form of 251 annotated OCT images obtained during the initial examination of patients who were treated with n-AMD using anti-VEGF therapy from 2014 to 2021 to develop a segmentation algorithm. The architecture of the neural network was a convolutional neural network UNET. To evaluate the effectiveness of the proposed model, the Dice coefficient (DSC) was used. Results. The segmentation accuracy showed high values for the determination of all biomarkers – from 0.97 to 0.99. For retinal pigment epithelium detachment, DSC shows a good value of 0.8. However, for the pigment epithelium and subretinal fluid, DSC values are 0.4, and for other biomarkers from 0.3 to 0.15. Conclusion. The obtained results of segmentation of OCT images showed a high accuracy of pixel determination (accuracy). The Dice coefficient showed good values for segmentation of retinal pigment epithelium detachment. Further research will focus on increasing the neural network training and validation dataset and improving segmentation accuracy for other biomarkers. Keywords: age-related macular degeneration, OCT, artificial intelligence, machine learning, biomarkers, anti-VEGF therapy