Fundus Autofluorescence Imaging Research Articles

Comparison of Geographic Atrophy Measurements Between Blue-Light Heidelberg Standard Field and Green-Light Optos Ultrawide Field Autofluorescence.

This study compared geographic atrophy (GA) measurements in the macula using standard 30° field and ultrawide field (UWF) fundus autofluorescence (FAF) imaging. Participants from Age-Related Eye Disease Study 2 (AREDS2) and Optos PEripheral RetinA (OPERA) studies with GA were included for comparison between standard field FAF with Heidelberg Spectralis and Optos 200Tx UWF FAF. Two time points 5years apart were evaluated. GA area (mm2) was recorded in the macular area for both imaging types and in the peripheral field for UWF. Of 102 paired images (73 subjects), the mean (SD) baseline GA area was 5.32 (6.36)mm2 with standard and 4.79 (5.87)mm2 with UWF FAF (P < 0.001). The mean difference between the two modalities was 0.52mm2 (95% confidence interval, -2.41 to 1.37). Progression of GA in 25 eyes over 5years showed a median annual growth rate of 1.28mm2 (range, 0.02 to 4.7) for standard and 1.34mm2 (range, 0.04 to 5.3) for UWF FAF (P = 0.49). The measurement of GA is larger on standard than on UWF FAF imaging. The observed difference may be due to image averaging and the use of blue versus green FAF. Similar GA progression with standard and UWF FAF suggests either may be used longitudinally, although not interchangeably. Further investigation is required with updated UWF technology. With the increasing adoption of UWF imaging modalities, this study suggests that Optos UWF FAF may be used longitudinally as an alternative to standard field FAF to monitor GA.

Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3500 Inherited Retinal Disease Patients from the United Kingdom

PurposeTo quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of patients with inherited retinal diseases (IRDs). DesignRetrospective study of imaging data (55-degree blue-light FAF, Heidelberg Spectralis) . ParticipantsPatients with a clinical and molecularly confirmed diagnosis of IRD who have undergone 55-degree FAF imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019. MethodsFive FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire imaging dataset. Main Outcome MeasuresQuantitative FAF imaging features including area in mm2 and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively. ResultsA total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. Across patients within our cohort at presentation, the five genes with the largest hypo-AF areas were CHM, ABCC6, RDH12, ABCA4, and RPE65, with mean per-patient areas of 43.72, 29.57, 20.07, 19.65, and 16.92 mm2. The five genes with the largest hyper-AF areas were BEST1, CDH23, NR2E3, MYO7A, and RDH12, with mean areas of 0.50, 047, 0.44, 0.38, and 0.33 mm2 respectively. The five genes with largest ring areas were NR2E3, CDH23, CRX, EYS and PDE6B, with mean areas of 3.60, 2.90, 2.89, 2.56, and 2.20 mm2. Vessel density was found to be highest in EFEMP1, BEST1, TIMP3, RS1, and PRPH2 (11.0%, 10.4%, 10.1%, 10.1%, 9.2%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes (RPGR, USH2A, RHO, EYS) found EYS to be the fastest progressor at -0.18 mm2/year. ConclusionsWe have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.

Association Between Arch-Shaped Hypo-Autofluorescent Lesions Detected Using Fundus Autofluorescence and Postoperative Hypotony.

Background: Chorioretinal folds are observed after vitrectomy due to ocular collapse caused by low intraocular pressure. The purpose of this study is to investigate the relationship between the postoperative hypotony, chorioretinal folds, and the fundus autofluorescence (FAF) findings. Methods: Two-hundred-and-seventy consecutive eyes that had undergone 25- or 27-gauge vitrectomy were examined. The associations between the arch-shaped hypo-autofluorescent lesions in the FAF images and the postoperative hypotony with intraocular pressure (IOP) ≤ 4 mmHg were determined on the day after the surgery. Results: Arch-shaped hypo-autofluorescent lesions were seen in 4 of the 270 eyes (1.5%), and hypo-autofluorescence was observed in 3 of 14 hypotonic eyes (18.5%). This was significantly more frequent than in the non-hypotony group (0.4%, p = 0.0004). Optical coherence tomography showed a loss of the ellipsoid zone and retinal pigment epithelial layer in the region of the arch-shaped lesions. None of the arch-shaped hypo-autofluorescent lesions involved the fovea, and the vision recovered in all cases. The hypo-autofluorescent lesions did not disappear during the 4 to 16 month observation period. Conclusions: The postoperative arch-shaped hypo-autofluorescent lesions were associated with postoperative hypotony and RPE damage due to chorioretinal folds. These findings remained even when the IOP was normalized and chorioretinal folds disappeared.

Rheumatic fever and long-term use of benzathine penicillin as possible risk factors for extensive macular atrophy with pseudodrusen in a Brazilian cohort

BackgroundAlthough there has been a large increase in the number of extensive macular atrophy with pseudodrusen (EMAP) cases, the basic aspects of this disease remain unknown. Brazilian patients have a common past history of rheumatic fever (RF) and/or benzathine penicillin (BP) treatment possibly related to the disease. We analyzed how RF and BP might be correlated with EMAP in Brazilian patients.DesignObservational, retrospective, case-control study.MethodsThe databases of three private eye clinics in Brazil were searched for patients with an EMAP-like appearance. Each patient was asked about a previous history of RF and/or long-term use of BP. Patients underwent best-corrected visual acuity (BCVA) measurement, color fundus imaging, fundus autofluorescence (FAF) imaging, optical coherence tomography (OCT) imaging, and electroretinography (ERG). The following characteristics were analyzed: subretinal drusenoid deposits (SDD), pigment mottling, retinal pigment epithelial/basement membrane (RPE/BM) separation, outer retinal or RPE atrophy, and identification of a paving stone-like appearance. The choroidal thickness was measured using enhanced depth imaging OCT. The central atrophic area was measured manually on ultra-wide-field FAF.ResultsA total of 154 eyes of 77 patients (women, 66.2%; mean age, 58.6 years) with EMAP were included; 90.9% of patients were diagnosed with RF; 94.8% had been treated with BP and treatment was started at an average age of 7.3 years (mean duration, 11.8 years). The treatment duration was significant for the area of atrophy (P = 0.027) in which each 1-year increase in treatment duration led to an average reduction of 6.91 mm2 in area. The age at diagnosis of RF was significant (P = 0.026) for SDD. The increase of 1 year in the diagnosis of RF (late disease) led to a reduction of 24% in the chance of central SDD being present. On OCT, 65.5% eyes had SDD and more than 70% had a split RPE/BM and outer retinal or RPE atrophy. The choroidal thickness in patients with EMAP was significantly (P < 0.001) thinner than the control group. The ERG was abnormal in all eyes.ConclusionThese findings may suggest a relation between RF and EMAP in Brazilian patients. Patients with EMAP should be questioned about a history of RF.

Role of fundus autofluorescence imaging in the management of submacular hemorrhage

PurposeTo evaluate the baseline characteristics of fundus autofluorescence (FAF) in patients with submacular hemorrhage (SMH).MethodsThis retrospective study included patients diagnosed with treatment-naive, foveal-involving subretinal hemorrhage (size > 2-disc diameters) of any etiology, presenting between June 2017 and June 2023. Only cases with good-quality color fundus photographs, optical coherence tomography (OCT) scans, and blue-light FAF images at baseline were included. SMH imaging characteristics were documented and correlated with treatment outcomes. A successful treatment outcome was defined as the reduction, displacement or clearance of the SMH from beneath the fovea.ResultsNineteen cases of SMH (13 males, 6 females), ranging from 14 to 85 years, were analyzed. Neovascular age-related macular degeneration (nAMD) was the most common etiology (n = 11, 58%). Baseline visual acuity ranged from 6/9 to counting fingers at ½ meter, with a median presentation time of 7 days from symptom onset (range: 1–57 days). Treatment success was observed in 13 eyes (68%). Hypoautofluoroscence on FAF was significantly associated with SMH resolution (p = 0.021). However, no association was found between treatment success and clinical hemorrhage characteristics (p = 0.222), OCT findings (p = 0.222), or specific treatments (p > 0.05). Hypoautofluoroscence on FAF was the sole predictor of treatment success, as demonstrated by Spearman’s correlation (r = 0.637; p = 0.003) and linear regression analysis (p = 0.003).ConclusionFAF, in conjunction with color fundus photography and OCT, may provide valuable insights for clinicians in formulating treatment strategies for patients with SMH. Hypoautofluoroscence on FAF was a significant predictor of successful SMH resolution in this study.

Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence

PurposeThe region of growth (ROG) of geographic atrophy (GA) throughout the macular area has an impact on visual outcomes. Here, we developed multiple deep learning (DL) models to predict the 1-year ROG of GA lesions using fundus autofluorescence (FAF) images. DesignIn this retrospective analysis, 3 types of models were developed using FAF images collected 6 months after baseline to predict the GA lesion area (segmented lesion mask) at 1.5 years, FAF images collected at baseline and 6 months to predict the GA lesion at 1.5 years, and FAF images collected 6 months after baseline to predict the GA lesion at 1 and 1.5 years. The 1-year ROG from the 6-month visit was derived by taking the difference between the GA lesion area (segmented lesion mask) at the 1.5-year and 6-month visits. ParticipantsPatients enrolled in the following lampalizumab clinical trials and prospective observational studies: NCT02247479, NCT02247531, NCT02479386, and NCT02399072. MethodsDatasets of study eyes from 597 patients were split into model training (310), validation (78), and test sets (209), stratified by baseline or initial lesion area, lesion growth rate, foveal involvement, and focality. DL experiments were performed using the 2-dimensional U-Net, whole-lesion and multiclass models were developed. Main Outcome MeasuresThe performance of the models was evaluated by calculating the Dice score, coefficient of determination (R2), and the squared Pearson correlation coefficient (r2) between the true and derived GA lesion 1-year ROG. ResultsThe model using baseline and 6-month FAF images to predict GA lesion enlargement at 1.5 years had the best performance for the derived 1-year ROG. Mean Dice scores were 0.73, 0.68, and 0.70, respectively, in the training, validation, and test sets. The R2 (0.77, 0.53, and 0.79) and r2 (0.83, 0.61, and 0.79) showed similar trends across the 3 sets. ConclusionsThese findings show the potential of using baseline and/or 6-month visit FAF images to predict 1-year GA ROG using a DL approach. This work could potentially help support decision making in clinical trials and more informed treatment decisions in clinical practice.

IMAGING PREDICTORS OF FUNCTIONAL OUTCOMES AFTER RHEGMATOGENOUS RETINAL DETACHMENT REPAIR.

To evaluate the microstructural optical coherence tomography and fundus autofluorescence imaging predictors of visual acuity, metamorphopsia, and aniseikonia following rhegmatogenous retinal detachment repair. This is a multicenter retrospective study of patients with primary rhegmatogenous retinal detachment who underwent repair. Best-corrected visual acuity, metamorphopsia, and aniseikonia were formally tested at 3 months postoperatively. Metamorphopsia and aniseikonia were quantitatively assessed with M-CHARTS and the New Aniseikonia Test, respectively. High-resolution spectral-domain optical coherence tomography and fundus autofluorescence images were obtained at 3 months postoperatively. Images were assessed for discontinuity of the outer retinal bands on optical coherence tomography and retinal displacement detected by retinal vessel printings on fundus autofluorescence by two masked graders with disagreements adjudicated by a third senior masked grader. Multiple linear regression models were used to determine the predictors of postoperative visual acuity, metamorphopsia, and aniseikonia. Six hundred fourteen eyes of 614 patients were included in this study. Regression analysis indicated that significant early postoperative (at 3 months) imaging predictors of visual acuity were discontinuity of the external limiting membrane (P = 0.001) and the presence of retinal vessel printings on fundus autofluorescence (P = 0.033). Discontinuity of interdigitation zone was a significant predictor of metamorphopsia [horizontal metamorphopsia (P =0.004); vertical metamorphopsia (P = 0.056); average of horizontal metamorphopsia + vertical metamorphopsia (P = 0.008)], and presence of retinal vessel printings was a significant predictor of aniseikonia (P = 0.04). Discontinuity of the external limiting membrane and retinal displacement were significant predictors of postoperative visual acuity following rhegmatogenous retinal detachment repair. Discontinuity of the interdigitation zone and retinal displacement were significant predictors of metamorphopsia and aniseikonia, respectively.

From onset to blindness: a comprehensive analysis of RPGR-associated X-linked retinopathy in a large cohort in China

BackgroundVariants in the RPGR are the leading cause of X-linked retinopathies (XLRPs). Further in-depth investigation is needed to understand the natural history.MethodsReview of all case records, molecular genetic testing results,...

Retinal displacement following direct versus indirect fluid exchange in pars Plana vitrectomy for rhegmatogenous retinal detachment.

To compare the occurrence and characteristics of retinal displacement following direct perfluorocarbon liquid (PFCL)-silicone oil (SO) exchange versus indirect PFCL for air and air for SO exchange methods during pars plana vitrectomy (PPV) in patients with rhegmatogenous retinal detachment (RRD). A comparative case series study was conducted on 58 eyes with recent RRD, undergoing standard three-port PPV with SO tamponade. Fluid exchange was performed using either direct or indirect methods. Postoperatively, a comprehensive ophthalmic examination and fundus autofluorescence (FAF) imaging were conducted at one month. The presence, amount, and direction of retinal displacement were assessed based on FAF imaging and compared between the direct and indirect fluid exchange groups. FAF imaging at one month revealed retinal displacement in 41.4% of eyes in the direct group and 62.1% in the indirect group, with no statistical difference between them (P = 0.537). However, the mean displacement was significantly higher in the indirect group (282.61 ± 110.83 µm) compared to the direct group (220.33 ± 39.67 µm, P = 0.04). The direction of displacement (downward or upward) did not differ significantly between the groups (P = 0.093). While the occurrence and location of postoperative retinal displacement did not significantly differ between direct and indirect fluid exchange methods during PPV for RRD, eyes treated with the direct method exhibited lower mean displacement compared to the indirect method. These findings suggest potential benefits of the direct exchange approach in minimizing retinal displacement following surgery.

Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy towards Comparative Performance on Optical Coherence Tomography as on Fundus Autofluorescence

Stargardt atrophy and geographic atrophy (GA) represent pivotal endpoints in FDA-approved clinical trials. Predicting atrophy progression is crucial for evaluating drug efficacy. Fundus autofluorescence (FAF), the standard 2D imaging modality in these trials, has limitations in patient comfort. In contrast, spectral-domain optical coherence tomography (SD-OCT), a 3D imaging modality, is more patient friendly but suffers from lower image quality. This study has two primary objectives: (1) develop an efficient predictive modeling for the generation of future FAF images and prediction of future Stargardt atrophic (as well as GA) regions and (2) develop an efficient predictive modeling with advanced 3D OCT features at ellipsoid zone (EZ) for the comparative performance in the generation of future enface EZ maps and prediction of future Stargardt atrophic regions on OCT as on FAF. To achieve these goals, we propose two deep neural networks (termed ReConNet and ReConNet-Ensemble) with recurrent learning units (long short-term memory, LSTM) integrating with a convolutional neural network (CNN) encoder–decoder architecture and concurrent learning units integrated by ensemble/multiple recurrent learning channels. The ReConNet, which incorporates LSTM connections with CNN, is developed for the first goal on longitudinal FAF. The ReConNet-Ensemble, which incorporates multiple recurrent learning channels based on enhanced EZ enface maps to capture higher-order inherent OCT EZ features, is developed for the second goal on longitudinal OCT. Using FAF images at months 0, 6, and 12 to predict atrophy at month 18, the ReConNet achieved mean (±standard deviation, SD) and median Dice coefficients of 0.895 (±0.086) and 0.922 for Stargardt atrophy and 0.864 (±0.113) and 0.893 for GA. Using SD-OCT images at months 0 and 6 to predict atrophy at month 12, the ReConNet-Ensemble achieved mean and median Dice coefficients of 0.882 (±0.101) and 0.906 for Stargardt atrophy. The prediction performance on OCT images is comparably good to that on FAF. These results underscore the potential of SD-OCT for efficient and practical assessment of atrophy progression in clinical trials and retina clinics, complementing or surpassing the widely used FAF imaging technique.

Automated identification of fleck lesions in Stargardt disease using deep learning enhances lesion detection sensitivity and enables morphometric analysis of flecks

PurposeTo classify fleck lesions and assess artificial intelligence (AI) in identifying flecks in Stargardt disease (STGD).MethodsA retrospective study of 170 eyes from 85 consecutive patients with confirmed STGD. Fundus autofluorescence...

Extent and Topography of Subretinal Drusenoid Deposits Associate With Rod-Mediated Vision in Aging and AMD: ALSTAR2 Baseline.

In AMD, rod-mediated dark adaptation (RMDA) at 5° eccentricity is slower in eyes with subretinal drusenoid deposits (SDDs) than in eyes without. Here we quantified SDD burden using supervised deep learning for comparison to vision and photoreceptor topography. In persons ≥60 years from the Alabama Study on Early Age-Related Macular Degeneration 2, normal, early AMD, and intermediate AMD eyes were classified by the AREDS nine-step system. A convolutional neural network was trained on 55°-wide near-infrared reflectance images for SDD segmentation. Trained graders annotated ground truth (SDD yes/no). Predicted and true datasets agreed (Dice coefficient, 0.92). Inference was manually proofread using optical coherence tomography. The mean SDD area (mm2) was compared among diagnostic groups (linear regression) and to vision (age-adjusted Spearman correlations). Fundus autofluorescence images were used to mask large vessels in SDD maps. In 428 eyes of 428 persons (normal, 218; early AMD, 120; intermediate AMD, 90), the mean SDD area differed by AMD severity (P < 0.0001): 0.16 ± 0.87 (normal), 2.48 ± 11.23 (early AMD), 11.97 ± 13.33 (intermediate AMD). Greater SDD area was associated with worse RMDA (r = 0.27; P < 0.0001), mesopic (r = -0.13; P = 0.02) and scotopic sensitivity (r = -0.17; P < 0.001). SDD topography peaked at 5° superior, extended beyond the Early Treatment of Diabetic Retinopathy Study grid and optic nerve, then decreased. SDD area is associated with degraded rod-mediated vision. RMDA 5° (superior retina) probes where SDD is maximal, closer to the foveal center than the rod peak at 3 to 6 mm (10.4°-20.8°) superior and the further eccentric peak of rod:cone ratio. Topographic data imply that factors in addition to rod density influence SDD formation.

Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3500 Inherited Retinal Disease Patients from the United Kingdom.

To quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients. Retrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients. Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone FAF 55-degree imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019. Five FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire MEH imaging dataset. Quantitative FAF imaging features including area in mm 2 and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively. A total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. The five genes with the largest hypo-AF areas were CHM , ABCC6 , ABCA4 , RDH12 , and RPE65 , with mean per-patient areas of 41.5, 30.0, 21.9, 21.4, and 15.1 mm 2 . The five genes with the largest hyper-AF areas were BEST1 , CDH23 , RDH12 , MYO7A , and NR2E3 , with mean areas of 0.49, 0.45, 0.44, 0.39, and 0.34 mm 2 respectively. The five genes with largest ring areas were CDH23 , NR2E3 , CRX , EYS and MYO7A, with mean areas of 3.63, 3.32, 2.84, 2.39, and 2.16 mm 2 . Vessel density was found to be highest in EFEMP1 , BEST1 , TIMP3 , RS1 , and PRPH2 (10.6%, 10.3%, 9.8%, 9.7%, 8.9%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes ( RPGR, USH2A, RHO, EYS ) found EYS to be the fastest progressor at -0.18 mm 2 /year. We have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.

Longitudinal assessment of female carriers of choroideremia using multimodal retinal imaging

Background/aimsFemale choroideremia carriers present with a spectrum of disease severity. Unlike in men, the rate of disease progression has not been well characterised in carriers. This longitudinal study aimed to...

Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate. Retrospective study with data from study eyes from three clinical trials (NCT02247479, NCT02247531, NCT02479386) in GA. The algorithm was initially trained with full FAF images, and its performance was considered benchmark. Ablation experiments investigated the contribution of imaging features to the performance of the algorithms. Three FAF image regions were defined relative to GA: Lesion, Rim, and Background. For No Lesion, No Rim, and No Background datasets, a single region of interest was removed at a time. For Lesion, Rim, and Background Shuffled datasets, individual region pixels were randomly shuffled. For Lesion, Rim, and Background Mask datasets, masks of the regions were used. A Convex Hull dataset was generated to evaluate the importance of lesion size. Squared Pearson correlation (r2) was used to compare the predictive performance of ablated datasets relative to the benchmark. The Rim region influenced r2 more than the other two regions in all experiments, indicating the most relevant contribution of this region to the performance of the algorithms. In addition, similar performance was observed for all regions when pixels were shuffled or only a mask was used, indicating intensity information was not independently informative without textural context. These ablation experiments enabled topographic clinical insights on FAF images from a DL-based GA progression prediction algorithm. Results from this study may lead to new insights on GA progression prediction.

PDE6A-Associated Retinitis Pigmentosa, Clinical Characteristics, Genetics, and Natural History

To analyze the genetics, clinical characteristics, and natural history of PDE6A-associated retinitis pigmentosa. Retrospective, longitudinal, observational cohort study. Patients with molecularly confirmed PDE6A-associated retinal dystrophy in a single tertiary referral center. Review of medical records and retinal imaging, including fundus autofluorescence (FAF) imaging and spectral-domain OCT. Genetic results were reviewed, and the detected variants were assessed. Molecular genetic testing, clinical findings including best-corrected visual acuity (BCVA), qualitative and quantitative retinal imaging analysis. Sixteen patients (32 eyes) were identified and evaluated longitudinally. Genetic analysis identified 14 variants in the PDE6A gene, including 8 novel variants. The mean age (± standard deviation, range) was 34.8 years (± 17.4, 12-76) at baseline, with a mean follow-up time of 4.8 years. Best-corrected visual acuity was 0.45 ± 0.45 logarithm of the minimum angle of resolution (logMAR) (range 0.0-1.6) at baseline and 0.65 ± 0.7 logMAR (range 0.0-2.3) at the last visit. Best-corrected visual acuity was similar among eyes in 88% of patients. A hyperautofluorescent ring was observed on FAF in 50% and 43.8% of the eyes at baseline and follow-up visit, respectively, with a mean area of 9.7 ± 4.5 mm2 at baseline and mean of 8.6 ± 4.8 mm2 at the follow-up visit. Mean horizontal ellipsoid zone width (EZW) at baseline was 1765 ± 1093 μm, which decreased to 1580 ± 1077 μm at follow-up. Eighteen eyes exhibited cystoid macular edema at baseline (56%), and 17 eyes (53%) at follow-up. There were statistically significant changes during the follow-up period in terms of BCVA, hyperautofluorescent ring area, and the EZW. This study highlights the natural history of PDE6A-retinopathy. Most patients in this cohort had mild BCVA loss, and slowly progressive disease, based on FAF and OCT measurements. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Application of Convolutional Gated Recurrent Units U-Net for Distinguishing between Retinitis Pigmentosa and Cone–Rod Dystrophy

Abstract Artificial Intelligence (AI) has gained a prominent role in the medical industry. The rapid development of the computer science field has caused AI to become a meaningful part of modern healthcare. Image-based analysis involving neural networks is a very important part of eye diagnoses. In this study, a new approach using Convolutional Gated Recurrent Units (GRU) U-Net was proposed for the classifying healthy cases and cases with retinitis pigmentosa (RP) and cone–rod dystrophy (CORD). The basis for the classification was the location of pigmentary changes within the retina and fundus autofluorescence (FAF) pattern, as the posterior pole or the periphery of the retina may be affected. The dataset, gathered in the Chair and Department of General and Pediatric Ophthalmology of Medical University in Lublin, consisted of 230 ultra-widefield pseudocolour (UWFP) and ultra-widefield FAF images, obtained using the Optos 200TX device (Optos PLC). The data were divided into three categories: healthy subjects (50 images), patients with CORD (48 images) and patients with RP (132 images). For applying deep learning classification, which rely on a large amount of data, the dataset was artificially enlarged using augmentation involving image manipulations. The final dataset contained 744 images. The proposed Convolutional GRU U-Net network was evaluated taking account of the following measures: accuracy, precision, sensitivity, specificity and F1. The proposed tool achieved high accuracy in a range of 91.00%–97.90%. The developed solution has a great potential in RP diagnoses as a supporting tool.

Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models.

AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, selection of artificial intelligence (AI) architecture is an important variable in model development. Here, we explore 12 distinct AI architecture combinations to determine the most effective approach for GA segmentation. We investigated various AI architectures, each with distinct combinations of encoders and decoders. The architectures included three decoders-FPN (Feature Pyramid Network), UNet, and PSPNet (Pyramid Scene Parsing Network)-and serve as the foundation framework for segmentation task. Encoders including EfficientNet, ResNet (Residual Networks), VGG (Visual Geometry Group) and Mix Vision Transformer (mViT) have a role in extracting optimum latent features for accurate GA segmentation. Performance was measured through comparison of GA areas between human and AI predictions and Dice Coefficient (DC). The training dataset included 601 FAF images from AREDS2 study and validation included 156 FAF images from the GlaxoSmithKline study. The mean absolute difference between grader measured and AI predicted areas ranged from -0.08 (95% CI = -1.35, 1.19) to 0.73 mm2 (95% CI = -5.75,4.29) and DC between 0.884-0.993. The best-performing models were UNet and FPN frameworks with mViT, and the least-performing models were PSPNet framework. The choice of AI architecture impacts GA segmentation performance. Vision transformers with FPN and UNet architectures demonstrate stronger suitability for this task compared to Convolutional Neural Network- and PSPNet-based models. Selecting an AI architecture must be tailored to the specific goals of the project, and developers should consider which architecture is ideal for their project.

Non-syndromic OTX2-associated pattern dystrophy: a 10-year multimodal imaging study

PurposeTo report novel multimodal imaging features and long-term follow-up of Orthodenticle Homeobox 2 (OTX2)-associated pattern Gdystrophy.MethodsA 14-year-old boy referred with glaucoma suspect and macular pigmentation underwent fundus autofluorescence imaging, optical coherence tomography, fluorescein and indocyanine green angiography, visual field test, microperimetry and electrophysiology over a ten-year period. Next-generation sequencing panel identified a de novo heterozygous likely pathogenic OTX2 variant, c.259G>A, [p.(Glu87Lys)].ResultsVisual acuity was 20/40 OD and 20/30 OS. Examination showed bilateral enlarged optic nerve heads and increased disc cupping, multiple cilioretinal arteries, a pigmentary maculopathy with stellate-shaped region of hypoautofluorescence, shallow serous macular detachment, subretinal deposits and temporal avascular retina. Angiography showed no source of leakage and absence of retinal neovascularisation despite extensive peripheral non perfusion. Electrophysiological assessments demonstrated mild progressive rod and cone pathway abnormalities, reduced light-adapted b:a ratio, and reduced Arden ratio on electro-oculogram. Ten-year follow-up confirmed a stable disease course despite persistent submacular fluid. There was no associated pituitary structural abnormality or dysfunction.ConclusionsThis case study contributes to further understanding of OTX2-associated pattern dystrophy, highlighting its stability over 10 years. Further investigation into inter-individual and intrafamilial variability is warranted.

Machine Learning for Extraction of Image Features Associated with Progression of Geographic Atrophy

Background: Several studies have investigated various features and models in order to understand the growth and progression of the ocular disease geographic atrophy (GA). Commonly assessed features include age, sex, smoking, alcohol consumption, sedentary lifestyle, hypertension, and diabetes. There have been inconsistencies regarding which features correlate with GA progression. Chief amongst these inconsistencies is whether the investigated features are readily available for analysis across various ophthalmic institutions. Methods:In this study, we focused our attention on the association of fundus autofluorescence (FAF) imaging features and GA progression. Our method included feature extraction using radiomic processes and feature ranking by machine learning incorporating the algorithm XGBoost to determine the best-ranked features. This led to the development of an image-based linear mixed-effects model, which was designed to account for slope change based on within-subject variability and inter-eye correlation. Metrics used to assess the linear mixed-effects model included marginal and conditional R2, Pearson’s correlation coefficient (r), root mean square error (RMSE), mean error (ME), mean absolute error (MAE), mean absolute deviation (MAD), the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and loglikelihood. Results: We developed a linear mixed-effects model with 15 image-based features. The model results were as follows: R2 = 0.96, r = 0.981, RMSE = 1.32, ME = −7.3 × 10−15, MAE = 0.94, MAD = 0.999, AIC = 2084.93, BIC = 2169.97, and log likelihood = −1022.46. Conclusions: The advantage of our method is that it relies on the inherent properties of the image itself, rather than the availability of clinical or demographic data. Thus, the image features discovered in this study are universally and readily available across the board.