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Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography.

ABSTRACT: Purpose:To develop deep learning (DL) models to predict best-corrected visual acuity (BCVA) from optical coherence tomography (OCT) images from patients with neovascular age-related macular degeneration (nAMD). Methods:Retrospective analysis of OCT images and associated BCVA measurements from the phase 3 HARBOR trial (NCT00891735). DL regression models were developed to predict BCVA at the concurrent visit and 12 months from baseline using OCT images. Binary classification models were developed to predict BCVA of Snellen equivalent of <20/40, <20/60, and ?20/200 at the concurrent visit and 12 months from baseline. Results:The regression model to predict BCVA at the concurrent visit had R 2 = 0.67 (root-mean-square error [RMSE] = 8.60) in study eyes and R 2 = 0.84 (RMSE = 9.01) in fellow eyes. The best classification model to predict BCVA at the concurrent visit had an area under the receiver operating characteristic curve (AUC) of 0.92 in study eyes and 0.98 in fellow eyes. The regression model to predict BCVA at month 12 using baseline OCT had R 2 = 0.33 (RMSE = 14.16) in study eyes and R 2 = 0.75 (RMSE = 11.27) in fellow eyes. The best classification model to predict BCVA at month 12 had AUC = 0.84 in study eyes and AUC = 0.96 in fellow eyes. Conclusions:DL shows promise in predicting BCVA from OCTs in nAMD. Further research should elucidate the utility of models in clinical settings. Translational Relevance:DL models predicting BCVA could be used to enhance understanding of structure-function relationships and develop more efficient clinical trials.

SUBMITTER: Kawczynski MG

PROVIDER: S-EPMC7488630 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography.

Kawczynski Michael G MG Bengtsson Thomas T Dai Jian J Hopkins J Jill JJ Gao Simon S SS Willis Jeffrey R JR

Translational vision science & technology 20200909 2

<h4>Purpose</h4>To develop deep learning (DL) models to predict best-corrected visual acuity (BCVA) from optical coherence tomography (OCT) images from patients with neovascular age-related macular degeneration (nAMD).<h4>Methods</h4>Retrospective analysis of OCT images and associated BCVA measurements from the phase 3 HARBOR trial (NCT00891735). DL regression models were developed to predict BCVA at the concurrent visit and 12 months from baseline using OCT images. Binary classification models ...[more]

PMID: 32974088

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Project description:PURPOSE:To assess parameters on optical coherence tomography (OCT), and their correlation with best-corrected visual acuity (BCVA) in patients with non-resolving central serous chorioretinopathy (CSC). METHODS:For 25 non-resolving CSC patients treated with photodynamic therapy (PDT), the thickness of retinal layers was assessed on the foveal spectral-domain (SD) OCT scan. Evaluated OCT parameters included the central retinal thickness (CRT), defined as the internal limiting membrane (ILM) to ellipsoid zone (EZ) distance, and the second band thickness (SBT), defined as the EZ to hyperreflective subretinal accumulation distance. Integrity of the external limiting membrane (ELM) and the EZ bands was also determined. These parameters, along with BCVA and CRT measured automatically by SD-OCT device software were obtained before PDT, after PDT, and at final visit. After Bonferroni correction, a p-value <0.007 was considered statistically significant. RESULTS:Twenty-five patients could be included at last visit before PDT and first visit after PDT. At final visit, 24 patients could be included, since 1 patients was lost to follow-up. Mean CRT was 112 μm at last visit before PDT, 118 μm at first visit after PDT (p = 0.030), and 127 μm at final visit (p<0.001compared to baseline). Mean SBT was 74 μm, 26 μm (p<0.001 compared to baseline), and 21 μm (p<0.001 compared to baseline), respectively. Mean BCVA in Early Treatment of Diabetic Retinopathy Study letters was 79 at baseline, 85 at first visit after PDT (p = 0.005 compared to baseline), and 87 at final visit (p = 0.001 compared to baseline). BCVA had an estimated correlation of β = 0.103 (p = 0.114) with CRT, β = -0.051 (p = 0.014) with SBT, β = 0.615 (p = 0.600) with the integrity of the ELM, and β = 4.917 with the integrity of the EZ (p = 0.001). CONCLUSIONS:In non-resolving CSC patients treated with half-dose PDT, the CRT increased at final visit in comparison to the last visit before PDT. The continuity of the EZ on SD-OCT was positively correlated with BCVA. We propose that the distance between ILM and EZ should be used as a reliable CRT measurement in non-resolving CSC patients treated with half-dose PDT.

Project description:BackgroundLow-contrast letter acuity and optical coherence tomography (OCT) capture visual dysfunction and axonal loss in adult-onset multiple sclerosis (MS), and have been proposed as secondary outcome metrics for therapeutic trials. Clinical trials will soon be launched in pediatric MS, but such outcome metrics have not been well-validated in this population.ObjectivesTo determine whether MS onset during childhood and adolescence is associated with measurable loss of visual acuity and thinning of the retinal nerve fiber layer (RNFL), whether such features are noted only in the context of clinical optic nerve inflammation (optic neuritis, ON) or are a feature of MS even in the absence of optic nerve relapses, and to define the optimal methods for such detection.Study designCross-sectional study.MethodsMonocular and binocular high- and low-contrast letter acuity and contrast sensitivity were assessed in a cross-sectional cohort of children (ages 5 to 17 years) with MS (N=22 patients, 44 eyes; 8 patients with a history of ON) and disease-free controls (N=29 patients; 58 eyes) from three academic centers. Binocular summation was determined by calculating the number of letters correctly identified using the binocular score minus the better eye score for each visual test. RNFL thickness was measured using OCT (Stratus OCT-3). Results were analyzed in terms of "eyes" as: MS ON+, MS ON-, and control eyes. Generalized estimating equation (GEE) regression models were used to compare patients to controls.ResultsTraditional high-contrast visual acuity scores did not differ between MS ON+, MS ON-, and controls eyes. MS ON+ eyes had decreased monocular (p<0.001) and decreased binocular (p=0.007) low-contrast letter acuity (Sloan 1.25% contrast charts) scores. Monocular visual acuity did not differ when comparing MS ON- and control eyes. The magnitude of binocular summation using low-contrast charts was similar for pediatric MS participants and controls and was not diminished in children with a history of ON. While the mean RNFL thickness for all MS eyes (103±17 μm) trended lower when compared to corresponding measures in control eyes (109±9 μm, p=0.085), we confirmed a highly significant reduction in mean RNFL thickness in MS eyes with a history of ON (86±22 μm, p<0.001). RNFL thickness of MS ON- eyes in pediatric MS patients (109±11 μm) did not differ from controls (p=0.994).ConclusionsLow-contrast letter acuity detects subtle visual loss in MS patients with prior ON, consistent with incomplete recovery, a finding further supported by RNFL loss in ON affected eyes. In MS patients with prior unilateral ON, binocular acuity is decreased; however, the magnitude of binocular summation is preserved, unlike adult-onset MS who exhibit a reduced capacity for visual compensation in the context of unilateral injury. Also unlike findings in adult-onset MS, we did not demonstrate RNFL thinning in ON- eyes of children and adolescents with MS. Further validation is required to confirm whether neurodegeneration of visual pathways occurs in the absence of relapse, and thus whether OCT will serve as a sensitive metric for such pathology in the pediatric and adolescent MS context.

Dataset Information

Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography.

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Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography.

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