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Artificial Intelligence and OCT Angiography in Full Thickness Macular Hole. New Developments for Personalized Medicine.

Authors :
Rizzo, Stanislao
Savastano, Alfonso
Lenkowicz, Jacopo
Savastano, Maria Cristina
Boldrini, Luca
Bacherini, Daniela
Falsini, Benedetto
Valentini, Vincenzo
Source :
Diagnostics (2075-4418); Dec2021, Vol. 11 Issue 12, p2319-2319, 1p
Publication Year :
2021

Abstract

Purpose: To evaluate the 1-year visual acuity predictive performance of an artificial intelligence (AI) based model applied to optical coherence tomography angiography (OCT-A) vascular layers scans from eyes with a full-thickness macular hole (FTMH). Methods: In this observational cross-sectional, single-center study, 35 eyes of 35 patients with FTMH were analyzed by OCT-A before and 1-year after surgery. Superficial vascular plexus (SVP) and deep vascular plexus (DVP) images were collected for the analysis. AI approach based on convolutional neural networks (CNN) was used to generate a continuous predictive variable based on both SVP and DPV. Different pre-trained CNN networks were used for feature extraction and compared for predictive accuracy. Results: Among the different tested models, the inception V3 network, applied on the combination of deep and superficial OCT-A images, showed the most significant differences between the two obtained image clusters defined in C1 and C2 (best-corrected visual acuity [BCVA] C1 = 49.10 [±18.60 SD] and BCVA C2 = 66.67 [±16.00 SD, p = 0.005]). Conclusions: The AI-based analysis of preoperative OCT-A images of eyes affected by FTMH may be a useful support system in setting up visual acuity recovery prediction. The combination of preoperative SVP and DVP images showed a significant morphological predictive performance for visual acuity recovery. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754418
Volume :
11
Issue :
12
Database :
Complementary Index
Journal :
Diagnostics (2075-4418)
Publication Type :
Academic Journal
Accession number :
154372232
Full Text :
https://doi.org/10.3390/diagnostics11122319