Automatic Visual Acuity Estimation by Means of Computational Vascularity Biomarkers Using Oct Angiographies

Díaz, Macarena; Díez-Sotelo, Marta; Gómez‐Ulla, Francisco; Novo, Jorge; Penedo, Manuel G.

doi:10.3390/s19214732

Cited by 3 publications

(3 citation statements)

References 27 publications

(34 reference statements)

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“…Each visit consists of four OCTA images representing different depths of the retina (superficial and deep) as well as different zoom levels (3 × 3 mm 2 and 6 × 6 mm 2 ). For a more detailed specification of the used dataset, see Reference [3].…”

Section: Methodsmentioning

confidence: 99%

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Fully Automatic Method for the Visual Acuity Estimation Using OCT Angiographies

Díaz

Novo

Penedo

2020

3rd XoveTIC Conference

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In this work we propose the automatic estimation of the visual acuity of patients with retinal vein occlusion using Optical Coherence Tomography by Angiography (OCTA) images. To do this, we first extract the most relevant biomarkers in this imaging modality—area of the foveal avascular zone and vascular densities in different regions of the OCTA image. Then, we use a support vector machine to estimate the visual acuity. We obtained a mean absolute error of 0.1713 between the manual visual acuity measurement and the estimated, being considered satisfactory results.

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Section: Methodsmentioning

confidence: 99%

“…Problems related to the loss of microvasculature lead to a consequent loss of vision. In this work, we present the estimation of the VA of patients with Retinal Vein Occlusion (RVO) using features that were extracted directly from the OCTA images [2,3].…”

Section: Introductionmentioning

confidence: 99%

Fully Automatic Method for the Visual Acuity Estimation Using OCT Angiographies

Díaz

Novo

Penedo

2020

3rd XoveTIC Conference

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“…5 All study participants had a protocol refraction, protocol VA measurement, and color macular photographs obtained when BCVA was determined. This estimation of fundusphoto-to-BCVA is a regression task, analogous to other investigations in medical AI, 21 and pursued recently in ophthalmology specifically for VA estimation from OCT angiography 22 or fundus images. 23 However, to our knowledge, BCVA estimation has not been investigated in the setting of DME wherein both protocol refraction and protocol VA measurements, as the gold standard, were obtained by trained personnel as primary outcome measurements for a regulatory agency; fundus photographs were obtained at the same visit as BCVA; and participants were followed up longitudinally.…”

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Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema

et al. 2023

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ImportanceBest-corrected visual acuity (BCVA) is a measure used to manage diabetic macular edema (DME), sometimes suggesting development of DME or consideration of initiating, repeating, withholding, or resuming treatment with anti–vascular endothelial growth factor. Using artificial intelligence (AI) to estimate BCVA from fundus images could help clinicians manage DME by reducing the personnel needed for refraction, the time presently required for assessing BCVA, or even the number of office visits if imaged remotely.ObjectiveTo evaluate the potential application of AI techniques for estimating BCVA from fundus photographs with and without ancillary information.Design, Setting, and ParticipantsDeidentified color fundus images taken after dilation were used post hoc to train AI systems to perform regression from image to BCVA and to evaluate resultant estimation errors. Participants were patients enrolled in the VISTA randomized clinical trial through 148 weeks wherein the study eye was treated with aflibercept or laser. The data from study participants included macular images, clinical information, and BCVA scores by trained examiners following protocol refraction and VA measurement on Early Treatment Diabetic Retinopathy Study (ETDRS) charts.Main OutcomesPrimary outcome was regression evaluated by mean absolute error (MAE); the secondary outcome included percentage of predictions within 10 letters, computed over the entire cohort as well as over subsets categorized by baseline BCVA, determined from baseline through the 148-week visit.ResultsAnalysis included 7185 macular color fundus images of the study and fellow eyes from 459 participants. Overall, the mean (SD) age was 62.2 (9.8) years, and 250 (54.5%) were male. The baseline BCVA score for the study eyes ranged from 73 to 24 letters (approximate Snellen equivalent 20/40 to 20/320). Using ResNet50 architecture, the MAE for the testing set (n = 641 images) was 9.66 (95% CI, 9.05-10.28); 33% of the values (95% CI, 30%-37%) were within 0 to 5 letters and 28% (95% CI, 25%-32%) within 6 to 10 letters. For BCVA of 100 letters or less but more than 80 letters (20/10 to 20/25, n = 161) and 80 letters or less but more than 55 letters (20/32 to 20/80, n = 309), the MAE was 8.84 letters (95% CI, 7.88-9.81) and 7.91 letters (95% CI, 7.28-8.53), respectively.Conclusions and RelevanceThis investigation suggests AI can estimate BCVA directly from fundus photographs in patients with DME, without refraction or subjective visual acuity measurements, often within 1 to 2 lines on an ETDRS chart, supporting this AI concept if additional improvements in estimates can be achieved.

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Comprehensive fully-automatic multi-depth grading of the clinical types of macular neovascularization in OCTA images

et al. 2023

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Optical Coherence Tomography Angiography or OCTA represents one of the main means of diagnosis of Age-related Macular Degeneration (AMD), the leading cause of blindness in developed countries. This eye disease is characterized by Macular Neovascularization (MNV), the formation of vessels that tear through the retinal tissues. Four types of MNV can be distinguished, each representing different levels of severity. Both the aggressiveness of the treatment and the recovery of the patient rely on an early detection and correct diagnosis of the stage of the disease. In this work, we propose the first fully-automatic grading methodology that considers all the four clinical types of MNV at the three most relevant OCTA scanning depths for the diagnosis of AMD. We perform both a comprehensive ablation study on the contribution of said depths and an analysis of the attention maps of the network in collaboration with experts of the domain. Our proposal aims to ease the diagnosis burden and decrease the influence of subjectivity on it, offering a explainable grading through the visualization of the attention of the expert models. Our grading proposal achieved satisfactory results with an AUC of 0.9224 ± 0.0381. Additionally, the qualitative analysis performed in collaboration with experts revealed the relevance of the avascular plexus in the grading of all three types of MNV (despite not being directly involved in some of them). Thus, our proposal is not only able to robustly detect MNV in complex scenarios, but also aided to discover previously unconsidered relationships between plexuses.

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Automatic Visual Acuity Estimation by Means of Computational Vascularity Biomarkers Using Oct Angiographies

Cited by 3 publications

References 27 publications

Fully Automatic Method for the Visual Acuity Estimation Using OCT Angiographies

Fully Automatic Method for the Visual Acuity Estimation Using OCT Angiographies

Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema

Comprehensive fully-automatic multi-depth grading of the clinical types of macular neovascularization in OCTA images

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