ArticleDiagnostics (Basel, Switzerland)2021
Artificial Intelligence and OCT Angiography in Full Thickness Macular Hole. New Developments for Personalized Medicine.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Extracellular Vesicles from the Probiotic YeastNutrients · 2026Article
- The Impact of Vitreoretinal Surgery in Patients with Uveitis: Current Strategies and Emerging Perspectives.Diagnostics (Basel, Switzerland) · 2026Review
- AI-Based Visual Prognosis in Full-Thickness Macular Hole Surgery Using the ILM Flap Technique.Beyoglu eye journal · 2026Article
- Artificial intelligence virtual assistants in primary eye care practice.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Non-Invasive Retinal Imaging Modalities for the Identification of Prognostic Factors in Vitreoretinal Surgery for Full-Thickness Macular Holes.Diagnostics (Basel, Switzerland) · 2023Review
- Correction: Rizzo et al. Artificial Intelligence and OCT Angiography in Full Thickness Macular Hole. New Developments for Personalized Medicine.Diagnostics (Basel, Switzerland) · 2022Article
- Applications of Optical Coherence Tomography in the Ocular Diagnosis: From the Tear Film to the Sclera.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence in ophthalmology.Romanian journal of ophthalmologyReview
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo 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).
methodsIn 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.
resultsAmong 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)).
conclusionsThe 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.
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