Sight threatening diabetic retinopathy in patients with macular telangiectasia type 2.

PURPOSE: Although diabetes is highly prevalent in patients with MacTel, progression to severe non-proliferative (NPDR) and proliferative diabetic retinopathy (PDR) is rarely reported. We report multimodal imaging features of sight-threatening diabetic retinopathy (STDR) in eyes with macular telangiectasia type 2 (MacTel). METHODS: Retrospective case series of seven participants of the MacTel Study at the Moorfields Eye Hospital NHS Foundation Trust study site and one patient from the Institute of Retina and Vitreous of Londrina, Brazil. Sight threatening diabetic retinopathy was defined as severe NPDR, PDR or diabetic macular edema. RESULTS: We report imaging features of 16 eyes of eight patients (7/8, 87.5% female) with diagnoses of MacTel and type 2 diabetes mellitus with STDR. Mean (SD) age was 56 (8.3) years. Patients were followed-up for a mean time of 9.1 (4.7) years. A total of 10/16 (62.5%) eyes showed PDR and 2/16 (12.5%) eyes presented a macular epiretinal neovascularization. CONCLUSIONS: People with diabetes mellitus and MacTel may not be protected from STDR as previously reported. Although the two diseases rarely co-exist, regular monitoring for diabetic retinopathy progression is recommended according to baseline retinopathy severity grades in line with established international guidelines. The presence of MacTel may not modify extended screening intervals, but there is no current evidence. The limited case series in the literature support treatment for complications and should follow the standard of care for either condition. Due to dual pathology, reactivation may be difficult to diagnose on standard imaging and multimodal imaging is recommended.

Developing a Continuous Severity Scale for Macular Telangiectasia Type 2 Using Deep Learning and Implications for Disease Grading.

PURPOSE: Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for macular telangiectasia (MacTel) type 2 by combining a DL classification model with uniform manifold approximation and projection (UMAP). DESIGN: We used a DL network to learn a feature representation of MacTel severity from discrete severity labels and applied UMAP to embed this feature representation into 2 dimensions, thereby creating a continuous MacTel severity scale. PARTICIPANTS: A total of 2003 OCT volumes were analyzed from 1089 MacTel Project participants. METHODS: We trained a multiview DL classifier using multiple B-scans from OCT volumes to learn a previously published discrete 7-step MacTel severity scale. The classifiers' last feature layer was extracted as input for UMAP, which embedded these features into a continuous 2-dimensional manifold. The DL classifier was assessed in terms of test accuracy. Rank correlation for the continuous UMAP scale against the previously published scale was calculated. Additionally, the UMAP scale was assessed in the κ agreement against 5 clinical experts on 100 pairs of patient volumes. For each pair of patient volumes, clinical experts were asked to select the volume with more severe MacTel disease and to compare them against the UMAP scale. MAIN OUTCOME MEASURES: Classification accuracy for the DL classifier and κ agreement versus clinical experts for UMAP. RESULTS: The multiview DL classifier achieved top 1 accuracy of 63.3% (186/294) on held-out test OCT volumes. The UMAP metric showed a clear continuous gradation of MacTel severity with a Spearman rank correlation of 0.84 with the previously published scale. Furthermore, the continuous UMAP metric achieved κ agreements of 0.56 to 0.63 with 5 clinical experts, which was comparable with interobserver κ values. CONCLUSIONS: Our UMAP embedding generated a continuous MacTel severity scale, without requiring continuous training labels. This technique can be applied to other diseases and may lead to more accurate diagnosis, improved understanding of disease progression, and key imaging features for pathologic characteristics. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A Novel Role for Corneal Pachymetry in Planning Cataract Surgery by Determining Changes in Spherical Equivalent Resulting from a Previous LASIK Treatment.

Objectives: To provide a metric to differentiate between hyperopic and myopic ablation of a prior LASIK treatment based on the corneal pachymetry profile after laser vision correction (LVC). Methods: Pachymetry data were retrospectively recovered from patients who had previous LASIK for refractive purposes between 2019 and 2020. Patients with any corneal disorder were excluded. Ablation spherical equivalent was predicted from the central to semiperipheral corneal thickness (CPT) ratio, both values were provided by using the Pentacam user interface software (UI), and values were computed from extracted raw pachymetry data. Results: Data of 157 eyes of 81 patients were collected, of which data were analysed for 73 eyes of 73 patients to avoid concurrence of measurements in both eyes per subject (42% female; mean age 40.9; SD 12.8). The CPT ratio cutoff for distinction between myopic and hyperopic LASIK was 0.86 for Pentacam UI data. Sensitivity and specificity were 0.7 and 0.95, respectively. Accuracy increased with computation of the CPT ratio based on extracted raw data with sensitivity and specificity of 0.87 and 0.99, respectively. There was a marked linear correlation between the CPT ratio and the ablation spherical equivalent (R2 = 0.93). Conclusions: CPT ratio cutoffs can correctly classify if a cornea previously had a hyperopic versus myopic LASIK surgery and estimate the ablation spherical equivalent of such treatment. This could prove useful for increased accuracy of intraocular lens (IOL) calculations for patients with no historical data of their prior LVC surgery at the time of cataract surgery planning.

Macular Telangiectasia Type 2: A Classification System Using MultiModal Imaging MacTel Project Report Number 10.

Purpose: To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging. Design: An algorithm was used on data from a prospective natural history study of MacTel for classification development. Subjects: A total of 1733 participants enrolled in an international natural history study of MacTel. Methods: The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity. Main Outcome Measures: The primary target of interest for the algorithm development by CART was the change in best-corrected visual acuity (BCVA) at baseline for the right and left eyes. These analyses using the algorithm were repeated for the BCVA obtained at the last study visit of the natural history study for the right and left eyes. Results: The CART analyses demonstrated 3 important features from the multimodal imaging for the classification: OCT hyper-reflectivity, pigment, and ellipsoid zone loss. By combining these 3 features (as absent, present, noncentral involvement, and central involvement of the macula), a 7-step scale was created, ranging from excellent to poor visual acuity. At grade 0, 3 features are not present. At the most severe grade, pigment and exudative neovascularization are present. To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed. Conclusions: This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Intraretinal pigmented cells in retinal degenerative disease.

PURPOSE: Invasion of pigmented cells into the retina occurs in retinal degenerative diseases, such as macular telangiectasia type 2 (MacTel) and retinitis pigmentosa (RP). These intraretinal pigmented cells may be derived from the retinal pigment epithelium (RPE), but differences and similarities between intraretinal pigmented cells and RPE have so far not been well characterised.Clinicopathologic case report. METHOD: Here, we compared intraretinal pigment cells with RPE cells by immunohistochemistry. Immunohistological stains for classic RPE markers (RPE65, CRALBP and KRT18) and blood vessel markers (lectin and collagen 4) were done on sections from postmortem eye tissue from two MacTel donors, an RP donor and a control donor. MAIN OUTCOME MEASURES: Presence of specific immunohistochemistry markers on intraretinal pigmented and RPE cells. RESULTS: We found that intraretinal pigmented cells did not express RPE65 and CRALBP, with a small subset expressing them weakly. However, they all expressed KRT18, which was also present in normal RPE cells. Interestingly, we also found clusters of KRT18-positive cells in the retina that were not pigmented. CONCLUSIONS: Our findings suggest that RPE cells invading the retina dedifferentiate (losing classic RPE markers) and can be pigmented or unpigmented. Therefore, the number of RPE cells invading the retina in retinal degenerative disease may be underappreciated by funduscopy.

Description of a patient cohort with Hereditary Sensory Neuropathy type 1 without retinal disease Macular Telangiectasia type 2 - implications for retinal screening in HSN1.

Pathogenic variants in the genes encoding serine palmitoyl transferase (SPTLC1 or SPTLC2) are the most common causes of the rare peripheral nerve disorder Hereditary Sensory Neuropathy Type 1 (HSN1). Macular telangiectasia type 2 (MacTel), a retinal disorder associated with disordered serine-glycine metabolism, has been described in some patients with HSN1. This study aims to further investigate this association in a cohort of people with HSN1. Fourteen patients with a clinically and genetically confirmed diagnosis of HSN1 from the National Hospital for Neurology and Neurosurgery (NHNN, University College London Hospitals NHS Foundation Trust, London, United Kingdom) were recruited to the MacTel Registry, between July 2018 and April 2019. Two additional patients were identified from the dataset of the international clinical registry study (www.lmri.net). Ocular examination included fundus autofluorescence, blue light and infrared reflectance, macular pigment optical density mapping and optical coherence tomography. Twelve patients had a pathogenic variant in the SPTLC1 gene, with p.Cys133Trp in 11 cases (92%) and p.Cys133Tyr in one case (8%). Four patients had a variant in the SPTLC2 gene. None of the patients showed clinical evidence of MacTel. The link between HSN1 and MacTel seems more complex than can solely be explained by the genetic variants. An extension of the spectrum of SPTLC1/2-related disease with phenotypic pleiotropy is proposed. HSN1 patients should be screened for visual symptoms and referred for specialist retinal screening, but the association of the two diseases is likely to be variable and remains unexplained.

State of the art spatial visualization of the response of neovascularisation to anti-vascular endothelial growth factor therapy.

PURPOSE: To visualize the mode of action of anti-vascular endothelial growth factor (anti-VEGFs) therapy on retinal neovascularization (RNV) in a patient with macular telangiectasia (MacTel) type 2 using a detailed three-dimensional data environment. OBSERVATION: A 60-year-old man presented with visual acuity loss and was diagnosed with MacTel type 2. Fluorescein angiography was not possible for safety reasons because of a history of severe reaction to fluorescein dye at his referring hospital. Optical coherence tomography angiography (OCTA) imaging revealed new retinal neovascular membranes (RNV) in the macula of both eyes. A marked reduction in the size of the RNV in both eyes was evident on volume-rendered three-dimensional OCTA retinal imaging after the first anti-VEGF injection. CONCLUSION AND IMPORTANCE: The ability to directly observe the effect of anti-VEGF injections on a RNV using three-dimensional OCTA was successfully demonstrated. This can be useful in patients with previous allergic and potentially lethal complications to fluorescein. In addition, enhanced three-dimensional spatial display of RNV leads to a greater understanding of the perfusion profile and the anatomical changes that occur in ocular neovascularization relative to surrounding tissue. This has the potential to provide insight into the pathobiology of angiogenesis.

Effect of ethnicity and other sociodemographic factors on attendance at diabetic eye screening: a 12-month retrospective cohort study.

OBJECTIVES: To examine the association of sociodemographic characteristics with attendance at diabetic eye screening in a large ethnically diverse urban population. DESIGN: Retrospective cohort study. SETTING: Screening visits in the North East London Diabetic Eye Screening Programme (NELDESP). PARTICIPANTS: 84 449 people with diabetes aged 12 years or older registered in the NELDESP and scheduled for screening between 1 April 2017 and 31 March 2018. MAIN OUTCOME MEASURE: Attendance at diabetic eye screening appointments. RESULTS: The mean age of people with diabetes was 60 years (SD 14.2 years), 53.4% were men, 41% South Asian, 29% White British and 17% Black; 83.4% attended screening. Black people with diabetes had similar levels of attendance compared with White British people. However, South Asian, Chinese and 'Any other Asian' background ethnicities showed greater odds of attendance compared with White British. When compared with their respective reference group, high levels of deprivation, younger age, longer duration of diabetes and worse visual acuity, were all associated with non-attendance. There was a higher likelihood of attendance per quintile improvement in deprivation (OR, 1.06; 95% CI, 1.03 to 1.08), with increasing age (OR per decade, 1.17; 95% CI, 1.15 to 1.19), with better visual acuity (OR per Bailey-Lovie chart line 1.12; 95% CI, 1.11 to 1.14) and with longer time of NELDESP registration (OR per year, 1.02; 95% CI, 1.01 to 1.03). CONCLUSION: Ethnic differences in diabetic eye screening uptake, though small, are evident. Despite preconceptions, a higher likelihood of screening attendance was observed among Asian ethnic groups when compared with the White ethnic group. Poorer socioeconomic profile was associated with higher likelihood of non-attendance for screening. Further work is needed to understand how to target individuals at risk of non-attendance and reduce inequalities.

Fundus Autofluorescence Imaging in Macular Telangiectasia Type 2: MacTel Study Report Number 9.

PURPOSE: To investigate the role of fundus autofluorescence (FAF) imaging in the diagnosis of macular telangiectasia type 2 (MacTel) and to describe disease-associated FAF patterns and their origin. DESIGN: Cross-sectional multicenter study METHODS: FAF images were collected from the multicenter MacTel Natural History Observation and Registry Study. In a first qualitative approach, common FAF phenotypes were defined and correlated with multimodal imaging. We then evaluated how many eyes showed FAF changes, and temporal vs nasal asymmetry of FAF changes was graded. Finally, 100 eyes of MacTel patients and 100 control eyes (50 normal eyes and 50 eyes with other macular diseases) were combined and 2 masked graders assessed the presence of MacTel based on FAF images alone. RESULTS: The study included 807 eyes of 420 patients (33 eyes were excluded owing to poor image quality). Loss of macular pigment, cystoid spaces, pigment plaques, neovascular membranes, and ectatic vascular changes commonly caused characteristic changes on FAF images. All MacTel patients had macular FAF changes in at least 1 eye. In 95% of eyes, these changes were more pronounced temporally than nasally. Common FAF patterns were increased (60%) and mixed/decreased FAF (38%) and/or visibility of vascular changes such as blunted vessels or ectatic capillaries (79%). Based on those features, high diagnostic performance was achieved for detection of the disease based on FAF alone (Youden index up to 0.91). CONCLUSIONS: The study demonstrates that MacTel is consistently associated with disease-specific changes on FAF imaging. Those changes are typically more pronounced in the temporal parafovea.

Identification of genetic factors influencing metabolic dysregulation and retinal support for MacTel, a retinal disorder.

Macular Telangiectasia Type 2 (MacTel) is a rare degenerative retinal disease with complex genetic architecture. We performed a genome-wide association study on 1,067 MacTel patients and 3,799 controls, which identified eight novel genome-wide significant loci (p < 5 × 10-8), and confirmed all three previously reported loci. Using MAGMA, eQTL and transcriptome-wide association analysis, we prioritised 48 genes implicated in serine-glycine biosynthesis, metabolite transport, and retinal vasculature and thickness. Mendelian randomization indicated a likely causative role of serine (FDR = 3.9 × 10-47) and glycine depletion (FDR = 0.006) as well as alanine abundance (FDR = 0.009). Polygenic risk scoring achieved an accuracy of 0.74 and was associated in UKBiobank with retinal damage (p = 0.009). This represents the largest genetic study on MacTel to date and further highlights genetically-induced systemic and tissue-specific metabolic dysregulation in MacTel patients, which impinges on retinal health.

Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning.

PURPOSE: We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD). DESIGN: Development and validation of a deep-learning model for feature segmentation. METHODS: Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2712 B-scans. A deep neural network was trained with these data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by 4 independent observers. The main outcome measures were Dice score, intraclass correlation coefficient, and free-response receiver operating characteristic curve. RESULTS: On 11 of 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared with 0.61 ± 0.17 for the observers. The mean intraclass correlation coefficient for the model was 0.66 ± 0.22, compared with 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively because of a lack of data. Free-response receiver operating characteristic analysis demonstrated that the model scored similar or higher sensitivity per false positives compared with the observers. CONCLUSIONS: The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.

Contextualizing single-arm trials with real-world data: An emulated target trial comparing therapies for neovascular age-related macular degeneration.

One-in-four ophthalmology trials are single-armed, which poses challenges to their interpretation. We demonstrate how real-world cohorts used as external/synthetic control arms can contextualize such trials. We herein emulated a target trial on the intention-to-treat efficacy of off-label bevacizumab (q6w) pro re nata relative to fixed-interval aflibercept (q8w) for improving week 54 visual acuity of eyes affected by neovascular age-related macular degeneration. The bevacizumab arm (n = 65) was taken from the ABC randomized controlled trial. A total of 4,471 aflibercept-treated eyes aligning with the ABC trial eligibility were identified from electronic health records and synthetic control arms were created by emulating randomization conditional on age, sex, and baseline visual read via exact matching and propensity score methods. We undertook an inferiority analysis on mean difference at 54 weeks; outcomes regression on achieving a change in visual acuity of greater than or equal to 15, greater than or equal to 10, and less than or equal to -15 Early Treatment Diabetic Retinopathy (ETDRS) letters at week 54; and a time-to-event analysis on achieving a change in visual acuity of greater than or equal to 15, greater than or equal to 10, and less than or equal to -15 ETDRS letters by week 54. The findings suggest off-label bevacizumab to be neither inferior nor superior to licensed aflibercept. Our study highlights how real-world cohorts representing the counterfactual intervention could aid the interpretation of single-armed trials when analyzed in accord to the target trial framework. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? One-in-four randomized controlled trials in ophthalmology are single-armed, which poses challenges for interpreting their efficacy relative to standard of care. Recent conceptual advances in the methods of causal inference and in the emulation of target trials suggests that the standard-of-care arms representing the counterfactual intervention can be approximated with observational data. WHAT QUESTION DID THIS STUDY ADDRESS? How real-world cohorts representing the counterfactual intervention can aid the interpretation of single-armed ophthalmological trials. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? Our study highlights how real-world cohorts representing the counterfactual intervention could aid the interpretation of single-armed ophthalmological trials when undertaken in accord with the target trial framework. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? External counterfactual arms could reduce the time and cost to reach potential regulatory approval.

HYPERREFLECTIVITY ON OPTICAL COHERENCE TOMOGRAPHY IN MACULAR TELANGIECTASIA TYPE 2.

PURPOSE: To define, characterize, and classify hyperreflectivity on optical coherence tomography and report its prevalence in macular telangiectasia Type 2. METHODS: In a primary cross-sectional analysis, multimodal imaging data were retrospectively analyzed. The definition of hyperreflectivity and neovascularization on optical coherence tomography followed optical coherence tomography angiography-based criteria. Eyes were graded for the presence of hyperreflectivity and neovascularization and further categorized into three classes based on position and extent of hyperreflectivity. In a secondary analysis, eyes were reviewed for ≥24 months using optical coherence tomography imaging. RESULTS: Three hundred and twenty-two eyes from 161 patients were analyzed in the cross-sectional analysis. Hyperreflectivity was found in 177 (55%) and neovascular membranes in 49 (15%) eyes. Hyperreflectivity correlated significantly with parameters indicative of disease progression. In the longitudinal analysis, 206 eyes from 103 patients were reviewed over a mean of 35.6 months. 17/86 eyes (20%) showed a de novo development of hyperreflectivity. 8/29 eyes (28%) with preexistent intraretinal hyperreflectivity developed outer retinal hyperreflectivity. A high proportion of eyes with outer retinal hyperreflectivity (17/52 [33%]) developed neovascular membranes. CONCLUSION: Hyperreflectivity represents a common finding in macular telangiectasia Type 2 but lacks a uniform definition. We propose a hyperreflectivity grading scale that may help to estimate disease progression and identify eyes at risk for developing neovascular membranes.

Right-angled vessels in macular telangiectasia type 2.

PURPOSE: To evaluate the role of right-angled vessels (RAVs) during disease progression in macular telangiectasia type 2 (MacTel). METHODS: In this study, 100 eyes of 52 patients and 52 eyes of 26 age-related controls were examined using fundus photography, spectral-domain optical coherence tomography (SD-OCT), OCT angiography (OCT-A) and fundus fluorescein angiography (FFA). Two masked readers graded fundus photographs of patients' eyes into five disease stages according to Gass and Blodi, and evaluated all eyes for the presence of RAVs. If RAVs were present, their course and origin (arterial vs venous) was evaluated with OCT-A and FFA, respectively. Additionally, we looked for morphological correlates of these vessels on SD-OCT scans. Neovascular eyes were analysed for the presence of RAVs and for morphological changes on formation of neovascularisations (NVs). RESULTS: In OCT-A, RAVs were already detectable in eyes with early stages (1 to 2), could be tracked from superficial to outer retinal layers and were shown to form anastomoses in the outer retina with disease progression. These vessels were of both arterial and venous origin as shown by early phase FFA. Dilated capillaries and RAVs in OCT-A corresponded to hyper-reflective alterations of the outer retina on SD-OCT scans. In 19/19 eyes, NVs were associated with the presence of RAVs, and RAVs were shown to directly connect to neovascular complexes and to undergo morphological changes upon NV formation. CONCLUSIONS: The results emphasise the role of RAVs during disease progression from an early stage on and demonstrate their involvement in the development of secondary NVs in MacTel.

Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

BACKGROUND: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading. METHODS: Cross-sectional study with consecutive recruitment of patients attending annual diabetic eye screening. Imaging with mydriasis was performed (two-field protocol) with the EIDON platform (CenterVue, Padua, Italy) and standard NDESP cameras. Human grading was carried out according to NDESP protocol. Images were processed by EyeArt V.2.1.0 (Eyenuk Inc, Woodland Hills, California). The reference standard for analysis was the human grade of standard NDESP images. RESULTS: We included 1257 patients. Sensitivity estimates for retinopathy grades were: EIDON images; 92.27% (95% CI: 88.43% to 94.69%) for any retinopathy, 99% (95% CI: 95.35% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. For NDESP images: 92.26% (95% CI: 88.37% to 94.69%) for any retinopathy, 100% (95% CI: 99.53% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. One case of vision-threatening retinopathy (R1M1) was missed by the EyeArt when analysing the EIDON images, but identified by the human graders. The EyeArt identified all cases of vision-threatening retinopathy in the standard images. CONCLUSION: EyeArt identified diabetic retinopathy in EIDON images with similar sensitivity to standard images in a large-scale screening programme, exceeding the sensitivity threshold recommended for a screening test. Further work to optimise the identification of 'no retinopathy' and to understand the differential lesion detection in the two imaging systems would enhance the use of these two innovative technologies in a diabetic retinopathy screening setting.

WITHDRAWN: Hyper-reflectivity on optical coherence tomography in macular telangiectasia type 2.

Ahead of Print article withdrawn by publisher.

Longitudinal Assessment of Remnant Foveal Cone Structure in a Case Series of Early Macular Telangiectasia Type 2.

Purpose: To determine the extent of remnant cone structure within early foveal ellipsoid zone (EZ) lesions in macular telangiectasia type 2 longitudinally using both confocal and split detector adaptive optics scanning light ophthalmoscopy (AOSLO). Methods: Spectral domain optical coherence tomography (SDOCT), confocal and split detector AOSLO were acquired from seven patients (10 eyes) with small (early) EZ lesions on SDOCT secondary to macular telangiectasia type 2 at baseline, 6 months, and 12 months. The presence of cone structure on AOSLO in areas of EZ loss as well as cones at 1° eccentricity, and their change over time were quantified. Results: By split detector AOSLO, remnant cone structure was identified within and on the borders of all foveal EZ lesions. Within the extent of these lesions, cone spacing ranged from 4.97 to 9.95 µm at baseline, 5.30 to 6.10 µm at 6 months, and 4.99 to 7.12 µm at 12 months. Four eyes with significantly smaller EZ lesions showed evidence of recovery of EZ reflectivity on SDOCT B-scans. Remnant cone structure was identified in some areas where EZ reflectivity recovered at the following time point. Eyes that showed recovery of EZ reflectivity had a continuous external limiting membrane. Conclusions: Remnant cone structure can persist within small SDOCT-defined EZ lesions, which can wax and wane in appearance over time. AOSLO can help to inform the interpretation of SDOCT imaging. Translational Relevance: The absence of EZ in early macular telangiectasia type 2 and other retinal conditions needs careful interpretation because it does not always indicate an absence of underlying cone structure. The integrity of the external limiting membrane may better predict the presence of remnant cone structure and recovery of EZ reflectivity.

[Artificial intelligence in ophthalmology : Guidelines for physicians for the critical evaluation of studies]. / Künstliche Intelligenz in der Augenheilkunde : Leitfaden für Ärzte zur kritischen Bewertung von Studien.

BACKGROUND: Empirical models have been an integral part of everyday clinical practice in ophthalmology since the introduction of the Sanders-Retzlaff-Kraff (SRK) formula. Recent developments in the field of statistical learning (artificial intelligence, AI) now enable an empirical approach to a wide range of ophthalmological questions with an unprecedented precision. OBJECTIVE: Which criteria must be considered for the evaluation of AI-related studies in ophthalmology? MATERIAL AND METHODS: Exemplary prediction of visual acuity (continuous outcome) and classification of healthy and diseased eyes (discrete outcome) using retrospectively compiled optical coherence tomography data (50 eyes of 50 patients, 50 healthy eyes of 50 subjects). The data were analyzed with nested cross-validation (for learning algorithm selection and hyperparameter optimization). RESULTS: Based on nested cross-validation for training, visual acuity could be predicted in the separate test data-set with a mean absolute error (MAE, 95% confidence interval, CI of 0.142 LogMAR [0.077; 0.207]). Healthy versus diseased eyes could be classified in the test data-set with an agreement of 0.92 (Cohen's kappa). The exemplary incorrect learning algorithm and variable selection resulted in an MAE for visual acuity prediction of 0.229 LogMAR [0.150; 0.309] for the test data-set. The drastic overfitting became obvious on comparison of the MAE with the null model MAE (0.235 LogMAR [0.148; 0.322]). CONCLUSION: Selection of an unsuitable measure of the goodness-of-fit, inadequate validation, or withholding of a null or reference model can obscure the actual goodness-of-fit of AI models. The illustrated pitfalls can help clinicians to identify such shortcomings.