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 = 66.67 (16.00 SD) and BCVA C2 = 49.10 (18.60 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.
Highlights
The role of advanced imaging analysis is becoming increasingly important in daily clinical practice and biomedical research due to recent advancements of radiomics and other artificial intelligence (AI)-based image analysis [1,2]
Clinical decision support systems are routinely incorporated into patient evaluation workflows, often including quantitative, multimodal imaging assessments integrating several variables coming from different omics domains according to the most innovative paradigms of personalized medicine [3]
Owing to the application of deep learning (DL) techniques, promising AI-based models have recently been developed in ophthalmology, which incorporates different types of imaging to predict diabetic retinopathy [6], glaucoma diagnosis [7], and agerelated macular degeneration [8]
Summary
The role of advanced imaging analysis is becoming increasingly important in daily clinical practice and biomedical research due to recent advancements of radiomics and other artificial intelligence (AI)-based image analysis [1,2].Clinical decision support systems are routinely incorporated into patient evaluation workflows, often including quantitative, multimodal imaging assessments integrating several variables coming from different omics domains according to the most innovative paradigms of personalized medicine [3].Ophthalmology, and retinal surgery, are exemplar subspecialties in which a remarkable use of several data sources (i.e., imaging, functional tests, electric retinal activity) is performed for the definition of comprehensive diagnosis, prognostic stratification, and follow-up strategies of patients affected by ocular diseases [4,5].Owing to the application of deep learning (DL) techniques, promising AI-based models have recently been developed in ophthalmology, which incorporates different types of imaging to predict diabetic retinopathy [6], glaucoma diagnosis [7], and agerelated macular degeneration [8].The most common image modality used in ophthalmology is to date represented by fundus photography. Clinical decision support systems are routinely incorporated into patient evaluation workflows, often including quantitative, multimodal imaging assessments integrating several variables coming from different omics domains according to the most innovative paradigms of personalized medicine [3]. Owing to the application of deep learning (DL) techniques, promising AI-based models have recently been developed in ophthalmology, which incorporates different types of imaging to predict diabetic retinopathy [6], glaucoma diagnosis [7], and agerelated macular degeneration [8]. Fundus photography has been characterized by increasing use in recent years, including screening for blindness in diabetic eyes and for glaucoma disease [9]
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