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To investigate the functional associations of intraretinal fluid (IRF) and subretinal fluid (SRF) volumes at baseline and after the loading dose as well as fluid change after the first injection with best-corrected visual acuity (BCVA) in patients with neovascular age-related macular degeneration (nAMD) who received an anti-VEGF treatment over 24 months.Post hoc analysis of a phase III, randomized, multicenter trial in which ranibizumab was administered monthly or in a pro re nata regimen (HARBOR).Study eyes of 1094 treatment-naïve patients with nAMD.IRF and SRF volumes were segmented automatically on monthly spectral domain OCT images. Fluid volumes and changes thereof were included as covariates into longitudinal mixed-effects models, which modeled BCVA trajectories.BCVA estimates corresponding to baseline, follow-up, and persistent IRF/SRF volumes after the loading dose; BCVA estimates of change in fluid volumes after the first injection; and marginal and conditional RAnalysis of 22 494 volumetric scans revealed that foveal IRF consistently shows a negative correlation with BCVA at baseline and subsequent visits (-3.23 and -4.32 letters/100 nL, respectively). After the first injection, BCVA increased by +2.13 letters/100 nL decrease in foveal IRF. Persistent IRF was associated with lower baseline BCVA and less improvement. Foveal SRF correlated with better BCVA at baseline and subsequent visits (+6.52 and +1.42 letters/100 nL, respectively). After the first injection, SRF decrease was associated with significant vision gain (+5.88 letters/100 nL). Foveal fluid correlated more with BCVA than parafoveal IRF/SRF.Although IRF consistently correlates with decreased function and recovery throughout therapy, SRF is associated with a more pronounced functional improvement. Moreover, SRF resolution provides increased benefit. Fluid-function correlation represents an essential base for the development of personalized treatment regimens, optimizing functional outcomes, and reducing treatment burden.

To investigate the impact of qualitatively graded and deep learning quantified imaging biomarkers on growth of geographic atrophy (GA) secondary to age-related macular degeneration.This prospective study included 1062 visits of 181 eyes of 100 patients with GA. Spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) images were acquired at each visit. Hyperreflective foci (HRF) were quantitatively assessed in SD-OCT volumes using a validated deep learning algorithm. FAF images were graded for FAF patterns, subretinal drusenoid deposits (SDD), GA lesion configuration and atrophy enlargement. Linear mixed models were calculated to investigate associations between all parameters and GA progression.FAF patterns were significantly associated with GA progression (p 0.001). SDD was associated with faster GA growth (p = 0.005). Eyes with higher HRF concentrations showed a trend towards faster GA progression (p = 0.072) and revealed a significant impact on GA enlargement in interaction with FAF patterns (p = 0.01). The fellow eye status had no significant effect on lesion enlargement (p 0.05). The diffuse-trickling FAF pattern exhibited significantly higher HRF concentrations than any other pattern (p 0.001).Among a wide range of investigated biomarkers, SDD and FAF patterns, particularly in interaction with HRF, significantly impact GA progression. Fully automated quantification of retinal imaging biomarkers such as HRF is both reliable and merited as HRF are indicators of retinal pigment epithelium dysmorphia, a central pathogenetic mechanism in GA. Identifying disease markers using the combination of FAF and SD-OCT is of high prognostic value and facilitates individualized patient management in a clinical setting.

Purpose To evaluate multimodal imaging findings of solitary idiopathic choroiditis (SIC; also known as unifocal helioid choroiditis) to clarify its origin, anatomic location, and natural course. Design Multicenter retrospective observational case series. Participants Sixty-three patients with SIC in 1 eye. Methods Demographic and clinical data were collected. Multimodal imaging included color fundus photography, OCT (including swept-source OCT), OCT angiography (OCTA), fundus autofluorescence, fluorescein and indocyanine green angiography, and B-scan ultrasonography. Main Outcome Measures Standardized grading of imaging features. Results Mean age at presentation was 56 ± 15 years (range, 12–83 years). Mean follow-up duration in 39 patients was 39 ± 55 months (range, 1 month–25 years). The lesions measured a mean of 2.4 × 2.1 mm in basal diameter, were located inferior (64%) or nasal to the optic disc, and appeared yellow (53%). No systemic associations were found. The lesions all appeared as an elevated subretinal mass, with OCT demonstrating all lesions to be confined to the sclera, not the choroid. On OCT, the deep lesion margin was visible in 12 eyes with a mean lesion thickness of 0.6 mm. Overlying choroidal thinning or absence was seen in 95% (mean choroidal thickness, 28 ± 35 μm). Mild subretinal fluid was observed overlying the lesions in 9 patients (14%). Retinal pigment epithelial disruption and overlying retinal thinning was observed in 56% and 57%, respectively. OCT angiography was performed in 13 eyes and demonstrated associated choroidal and lesional flow voids. Four lesions (6%) were identified at the macula, leading to visual loss in 1 patient. One lesion demonstrated growth and another lesion showed spontaneous resolution. Conclusions In this largest series to date, multimodal imaging of SIC demonstrated a scleral location in all patients. The yellow and white clinical appearance may be related to scleral unmasking resulting from atrophy of overlying tissues. Additional associated features included documentation of deep margin on swept-source OCT, trace subretinal fluid in a few patients, and OCTA evidence of lesional flow voids. Because of the scleral location of this lesion in every patient, a new name, focal scleral nodule, is proposed.

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.

To quantitatively measure hyperreflective foci (HRF) during the progression of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) using deep learning (DL) and investigate the association with local and global growth of GA.Eyes with GA were prospectively included. Spectral-domain optical coherence tomography (SDOCT) and fundus autofluorescence images were acquired every 6 months. A 500-μm-wide junctional zone adjacent to the GA border was delineated and HRF were quantified using a validated DL algorithm. HRF concentrations in progressing and nonprogressing areas, as well as correlations between HRF quantifications and global and local GA progression, were assessed.A total of 491 SDOCT volumes from 87 eyes of 54 patients were assessed with a median follow-up of 28 months. Two-thirds of HRF were localized within a millimeter adjacent to the GA border. HRF concentration was positively correlated with GA progression in unifocal and multifocal GA (all P.001) and de novo GA development (P = .037). Local progression speed correlated positively with local increase of HRF (P value range.001-.004). Global progression speed, however, did not correlate with HRF concentrations (P.05). Changes in HRF over time did not have an impact on the growth in GA (P.05).Advanced artificial intelligence (AI) methods in high-resolution retinal imaging allows to identify, localize, and quantify biomarkers such as HRF. Increased HRF concentrations in the junctional zone and future macular atrophy may represent progressive migration and loss of retinal pigment epithelium. AI-based biomarker monitoring may pave the way into the era of individualized risk assessment and objective decision-making processes. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Verfahren der kunstlichen Intelligenz (KI) wie kunstliche neuronale Netzwerke zeigen vielversprechende Ergebnisse im Bereich der automatisierten Analyse ophthalmologischer Bilddaten. Der vorliegende Beitrag diskutiert, inwiefern die Diskussion und Anwendung von KI-Algorithmen auch zur Qualitatssicherung im Bereich der Augenheilkunde beitragen kann. Relevante Literaturbeitrage werden zusammenfassend diskutiert. Systeme, basierend auf kunstlichen neuronalen Netzen, erzielen sehr gute Ergebnisse in der Diagnostik von Augenerkrankungen, indem beispielsweise OCT(optische Koharenztomographie)-Bilder sehr gut segmentiert und Lasionsbestandteile detektiert werden konnen. Zum Trainieren dieser Netzwerke werden grose Datensatze genutzt. Qualitat und Verfugbarkeit dieser Datensatze bestimmen die kontinuierliche Verbesserung der Algorithmen. Der Vergleich zwischen KI-Algorithmen und der arztlichen Bildinterpretation hat auch Einblicke in die diagnostische Ubereinstimmung zwischen ArztInnen erlaubt. Aktuelle Herausforderungen liegen in der Entwicklung von Verfahren zur Modellierung der Unsicherheit und besseren Nachvollziehbarkeit automatisierter diagnostischer Entscheidungen. KI-Systeme konnten zeitnah als Systeme zur Entscheidungsunterstutzung zum Einsatz kommen. Dadurch konnen sie zur effizienteren Qualitatssicherung beitragen.

Purpose To prospectively evaluate the outcomes of different subtypes of neovascular age-related macular degeneration during intravitreal aflibercept monotherapy. Methods Forty-four eyes of 44 patients with treatment-naive polypoidal choroidal vasculopathy (PCV, n = 12), hemorrhagic choroidal neovascularization (hCNV, n = 12), pigment epithelium detachment (PED, n = 11), or retinal angiomatous proliferation (RAP, n = 9) were included and followed for 12 months. All patients received intravitreal aflibercept monotherapy. Results Mean visual acuity at baseline in PCV was 67 ± 16 Early Treatment Diabetic Retinopathy Study letters (20/50 Snellen equivalent), in hCNV 55 ± 21 (20/80), in RAP lesions 64 ± 11 (20/50), and in PED 74 ± 7 (20/32). At Month 12, visual acuity in PCV was 66 ± 16 (20/50), in hCNV 69 ± 17 (20/40), in RAP 68 ± 12 (20/50), and in PED 69 ± 18 (20/40). At the 12-month follow-up, visual acuity improved or was stable (±5 letters from baseline) in 84% of eyes (37/44 patients), with hCNV showing the greatest mean visual acuity gain. Mean central retinal thickness in patients with PCV was 523 ± 251 µm, in hCNV 497 ± 171, in RAP lesions 573 ± 132, and in PED 541 ± 158 and decreased to 310 ± 91 µm in PCV, 323 ± 75 µm in hCNV, 357 ± 173 µm in RAP lesions, and 422 ± 150 µm in PED. The mean area of atrophy increased from 2.0 ± 3.6 mm2 at baseline to 4.6 ± 8.6 mm2 at Month 12 (mean difference [95% confidence interval] -0.8 [-8.5 to 7.0], P = 0.8), with the greatest atrophy in patients with PED at Month 12. Conclusion All subtypes of neovascular age-related macular degeneration showed anatomical improvement and stabilization of visual function during intravitreal treatment.

The currently used measures of retinal function are limited by being subjective, nonlocalized, or taxing for patients. To address these limitations, we sought to develop and evaluate a deep learning (DL) method to automatically predict the functional end point (retinal sensitivity) based on structural OCT images.Retrospective, cross-sectional study.In total, 714 volumes of 289 patients were used in this study.A DL algorithm was developed to automatically predict a comprehensive retinal sensitivity map from an OCT volume. Four hundred sixty-three spectral-domain OCT volumes from 174 patients and their corresponding microperimetry examinations (Nidek MP-1) were used for development and internal validation, with a total of 15 563 retinal sensitivity measurements. The patients presented with a healthy macula, early or intermediate age-related macular degeneration, choroidal neovascularization, or geographic atrophy. In addition, an external validation was performed using 251 volumes of 115 patients, comprising 3 different patient populations: those with diabetic macular edema, retinal vein occlusion, or epiretinal membrane.We evaluated the performance of the algorithm using the mean absolute error (MAE), limits of agreement (LoA), and correlation coefficients of point-wise sensitivity (PWS) and mean sensitivity (MS).The algorithm achieved an MAE of 2.34 dB and 1.30 dB, an LoA of 5.70 and 3.07, a Pearson correlation coefficient of 0.66 and 0.84, and a Spearman correlation coefficient of 0.68 and 0.83 for PWS and MS, respectively. In the external test set, the method achieved an MAE of 2.73 dB and 1.66 dB for PWS and MS, respectively.The proposed approach allows the prediction of retinal function at each measured location directly based on an OCT scan, demonstrating how structural imaging can serve as a surrogate of visual function. Prospectively, the approach may help to complement retinal function measures, explore the association between image-based information and retinal functionality, improve disease progression monitoring, and provide objective surrogate measures for future clinical trials.

AimsTo investigate the impact of posterior vitreous detachment (PVD) on the efficacy of treat-and-extend (T&E) ranibizumab in neovascular age-related macular degeneration.MethodsIn a post hoc analysis of a randomised controlled clinical trial, spectral-domain optical coherence tomography images of treatment-naïve patients randomised to receive T&E (n=265) or monthly (n=264) ranibizumab for 12 months were included. Certified, masked graders diagnosed the presence or the absence of complete PVD. The main outcome measures were the mean change in best-corrected visual acuity (BCVA) and central retinal thickness (CRT) at month 12, the number of administered ranibizumab injections and the proportion of patients extended to more than 8 weeks.ResultsAt baseline, complete PVD was present in 51% and 56% of patients in the monthly and T&E arms, respectively. Mean change in BCVA at month 12 was +9.0 (PVD) vs +9.5 letters (no PVD, p=0.78) in monthly treated eyes, and +6.0 (PVD) vs +7.5 letters (no PVD, p=0.42) in T&E treated eyes. Conversely, mean change in CRT at month 12 was −174 (PVD) vs −173 µm (no PVD, p=0.98) in the monthly arm, and −175 (PVD) vs −164 µm (no PVD, p=0.58) in the T&E arm. In T&E treated patients, the median number of injections was eight vs nine (p=0.035). 71% of PVD eyes were extended successfully, compared with 55% of eyes without PVD (p=0.005).ConclusionPVD was not found to impact functional and anatomical outcomes of T&E ranibizumab therapy. However, patients without a complete PVD required more retreatments and were significantly less likely to be successfully extended.Trial registration numberNCT01948830

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed. However, it is currently not clear how successful they are in interpreting the retinal fluid on OCT, which is due to the lack of standardized benchmarks. To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams participating. The challenge consisted of two tasks: fluid detection and fluid segmentation. It featured for the first time: all three retinal fluid types, with annotated images provided by two clinical centers, which were acquired with the three most common OCT device vendors from patients with two different retinal diseases. The analysis revealed that in the detection task, the performance on the automated fluid detection was within the inter-grader variability. However, in the segmentation task, fusing the automated methods produced segmentations that were superior to all individual methods, indicating the need for further improvements in the segmentation performance.

Age-related macular degeneration (AMD) is the predominant cause of vision loss in the elderly with a major impact on ageing societies and healthcare systems. A major challenge in AMD management is the difficulty to determine the disease stage, the highly variable progression speed and the risk of conversion to advanced AMD, where irreversible functional loss occurs. In this study we developed an optical coherence tomography (OCT) imaging based spatio-temporal reference frame to characterize the morphologic progression of intermediate age-related macular degeneration (AMD) and to identify distinctive patterns of conversion to the advanced stages macular neovascularization (MNV) and macular atrophy (MA). We included 10,040 OCT volumes of 518 eyes with intermediate AMD acquired according to a standardized protocol in monthly intervals over two years. Two independent masked retina specialists determined the time of conversion to MNV or MA. All scans were aligned to a common reference frame by intra-patient and inter-patient registration. Automated segmentations of retinal layers and the choroid were computed and en-face maps were transformed into the common reference frame. Population maps were constructed in the subgroups converting to MNV (n=135), MA (n=50) and in non-progressors (n=333). Topographically resolved maps of changes were computed and tested for statistical significant differences. The development over time was analysed by a joint model accounting for longitudinal and right-censoring aspect. Significantly enhanced thinning of the outer nuclear layer (ONL) and retinal pigment epithelium (RPE)–photoreceptorinner segment/outer segment (PR-IS/OS) layers within the central 3 mm and a faster thinning speed preceding conversion was documented for MA progressors. Converters to MNV presented an accelerated thinning of the choroid and appearance changes in the choroid prior to MNV onset. The large-scale automated image analysis allowed us to distinctly assess the progression of morphologic changes in intermediate AMD based on conventional OCT imaging. Distinct topographic and temporal patterns allow to prospectively determine eyes with risk of progression and thereby greatly improving early detection, prevention and development of novel therapeutic strategies.

Nanophthalmos-4 is a rare autosomal dominant disorder caused by two known variations in TMEM98. An Austrian Caucasian pedigree was identified suffering from nanophthalmos and late onset angle-closure glaucoma and premature loss of visual acuity. Whole exome sequencing identified segregation of a c.602G > C transversion in TMEM98 (p.Arg201Pro) as potentially causative. A protein homology model generated showed a TMEM98 structure comprising α4, α5/6, α7 and α8 antiparallel helix bundles and two predicted transmembrane domains in α1 and α7 that have been confirmed in vitro. Both p.Arg201Pro and the two missense variations representing proline insertions identified previously to cause nanophthalmos-4 (p.Ala193Pro and p.His196Pro) are located in the charge polarized helix α8 (p.183-p210). Stability of the C-terminal alpha helical structure of TMEM98 is therefore essential to prevent the development of human nanophthalmos-4. Precise molecular diagnosis could lead to the development of tailored therapies for patients with orphan ocular disease.

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.Which criteria must be considered for the evaluation of AI-related studies in ophthalmology?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).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]).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.HINTERGRUND: Empirische Modelle sind seit Einführung der SRK(Sanders-Retzlaff-Kraff)-Formel im klinischen Alltag der Augenheilkunde etabliert. Rezente Entwicklungen im Bereich des statistischen Lernens („künstliche Intelligenz“ [KI]) ermöglichen jetzt ein empirisches Vorgehen für vielfältigste ophthalmologische Fragestellungen bei bislang unerreichter Präzision.Welche Kriterien müssen für die Bewertung von Arbeiten zum Thema KI in der Augenheilkunde berücksichtigt werden?Es erfolgen die beispielhafte Vorhersage des Visus (stetige Zielgröße) und Klassifikation von gesunden und kranken Augen (diskrete Zielgröße) anhand von retrospektiven optischen Kohärenztomographiebilddaten (50 Augen von 50 Patienten, 50 gesunde Augen von 50 Probanden). Die Daten wurden mit verschachtelter Kreuzvalidierung (zur Lernalgorithmusauswahl und Hyperparameteroptimierung) analysiert.Durch verschachtelte Kreuzvalidierung ließ sich der Visus im separaten Testdatensatz mit einem mittleren absoluten Fehler (MAE, [95 %-CI, Konfidenzintervall]) von 0,142 LogMAR [0,077; 0,207] vorhersagen. Kranke und gesunde Augen ließen sich im Testdatensatz mit einer Konkordanz von (Kappa nach Cohen) 0,92 klassifizieren. Die beispielhafte inkorrekte Lernalgorithmus- und Variablenauswahl resultierte in einem MAE von 0,229 LogMAR [0,150; 0,309] für den Testdatensatz. Erst durch Vergleich mit dem MAE des Nullmodells (0,235 LogMAR [0,148; 0,322]) wurde die Überanpassung offensichtlich.Die Auswahl einer ungeeigneten Kennzahl für die Anpassungsgüte, inadäquate Validierung oder Unterschlagen eines Null- oder Referenzmodells kann die tatsächliche Anpassungsgüte von KI-Modellen verschleiern. Die illustrierten Fallstricke können Klinikern und Forschern helfen, solche Unzulänglichkeiten zu erkennen.

Artificial intelligence has recently made a disruptive impact in medical imaging by successfully automatizing expert-level diagnostic tasks. However, replicating human-made decisions may inherently be biased by the fallible and dogmatic nature of human experts, in addition to requiring prohibitive amounts of training data. In this paper, we introduce an unsupervised deep learning architecture particularly designed for OCT representations for unbiased, purely data-driven biomarker discovery. We developed artificial intelligence technology that provides biomarker candidates without any restricting input or domain knowledge beyond raw images. Analyzing 54,900 retinal optical coherence tomography (OCT) volume scans of 1094 patients with age-related macular degeneration, we generated a vocabulary of 20 local and global markers capturing characteristic retinal patterns. The resulting markers were validated by linking them with clinical outcomes (visual acuity, lesion activity and retinal morphology) using correlation and machine learning regression. The newly identified features correlated well with specific biomarkers traditionally used in clinical practice (r up to 0.73), and outperformed them in correlating with visual acuity ($$\hbox {R}^{2} = 0.46$$R2=0.46 compared to $$\hbox {R}^{2} = 0.29$$R2=0.29 for conventional markers), despite representing an enormous compression of OCT imaging data (67 million voxels to 20 features). In addition, our method also discovered hitherto unknown, clinically relevant biomarker candidates. The presented deep learning approach identified known as well as novel medical imaging biomarkers without any prior domain knowledge. Similar approaches may be worthwhile across other medical imaging fields.

The authors of “Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT” which appeared in the January 2020 issue of this journal [1] would like to provide an updated Fig. 3 because there was an error in the published version. The output of the last convolutional layers says “2” in the number of channels but it should be “11” (10 retinal layer and the background).

Diabetic macular edema (DME) and retina vein occlusion (RVO) are macular diseases in which central photoreceptors are affected due to pathological accumulation of fluid. Optical coherence tomography allows to visually assess and evaluate photoreceptor integrity, whose alteration has been observed as an important biomarker of both diseases. However, the manual quantification of this layered structure is challenging, tedious and time-consuming. In this paper we introduce a deep learning approach for automatically segmenting and characterising photoreceptor alteration. The photoreceptor layer is segmented using an ensemble of four different convolutional neural networks. En-face representations of the layer thickness are produced to characterize the photoreceptors. The pixel-wise standard deviation of the score maps produced by the individual models is also taken to indicate areas of photoreceptor abnormality or ambiguous results. Experimental results showed that our ensemble is able to produce results in pair with a human expert, outperforming each of its constitutive models. No statistically significant differences were observed between mean thickness estimates obtained from automated and manually generated annotations. Therefore, our model is able to reliable quantify photoreceptors, which can be used to improve prognosis and managment of macular diseases.

Procedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data.This article discusses to what extent the application of AI algorithms can contribute to quality assurance in the field of ophthalmology.Relevant aspects from the literature are discussed.Systems based on artificial deep neural networks achieve remarkable results in the diagnostics of eye diseases, such as diabetic retinopathy and are very helpful, for example by segmenting optical coherence tomographic (OCT) images and detecting lesion components with high fidelity. To train these algorithms large data sets are required. The quality and availability of such data sets determine the continuous improvement of the algorithms. The comparison between the AI algorithms and physicians for image interpretation has also enabled insights into the diagnostic concordance between physicians. Current challenges include the development of methods for modelling decision uncertainty and improved interpretability of automated diagnostic decisions.Systems based on AI can support decision making for physicians and thereby contribute to a more efficient quality assurance.

To evaluate multimodal imaging findings of solitary idiopathic choroiditis (SIC; also known as unifocal helioid choroiditis) to clarify its origin, anatomic location, and natural course.Multicenter retrospective observational case series.Sixty-three patients with SIC in 1 eye.Demographic and clinical data were collected. Multimodal imaging included color fundus photography, OCT (including swept-source OCT), OCT angiography (OCTA), fundus autofluorescence, fluorescein and indocyanine green angiography, and B-scan ultrasonography.Standardized grading of imaging features.Mean age at presentation was 56 ± 15 years (range, 12-83 years). Mean follow-up duration in 39 patients was 39 ± 55 months (range, 1 month-25 years). The lesions measured a mean of 2.4 × 2.1 mm in basal diameter, were located inferior (64%) or nasal to the optic disc, and appeared yellow (53%). No systemic associations were found. The lesions all appeared as an elevated subretinal mass, with OCT demonstrating all lesions to be confined to the sclera, not the choroid. On OCT, the deep lesion margin was visible in 12 eyes with a mean lesion thickness of 0.6 mm. Overlying choroidal thinning or absence was seen in 95% (mean choroidal thickness, 28 ± 35 μm). Mild subretinal fluid was observed overlying the lesions in 9 patients (14%). Retinal pigment epithelial disruption and overlying retinal thinning was observed in 56% and 57%, respectively. OCT angiography was performed in 13 eyes and demonstrated associated choroidal and lesional flow voids. Four lesions (6%) were identified at the macula, leading to visual loss in 1 patient. One lesion demonstrated growth and another lesion showed spontaneous resolution.In this largest series to date, multimodal imaging of SIC demonstrated a scleral location in all patients. The yellow and white clinical appearance may be related to scleral unmasking resulting from atrophy of overlying tissues. Additional associated features included documentation of deep margin on swept-source OCT, trace subretinal fluid in a few patients, and OCTA evidence of lesional flow voids. Because of the scleral location of this lesion in every patient, a new name, focal scleral nodule, is proposed.

The automated analysis and quantification of retinal optical coherence tomography (OCT) images have seen tremendous advances in recent years. While it was only possible to measure retinal thickness previously, developments in artificial intelligence and particularly in deep learning have resulted in the availability of a large array of image analysis methods in a research setting today. It has become possible to quantify virtually every change seen in OCT images in a reproducible and automated manner, and it is only a matter of time until these advanced tools will be integrated into the every-day OCT analysis software. In addition, artificial intelligence also allows prognostic evaluation of images with regards to a patient’s individual future disease course or therapeutic outcome, introducing personalized healthcare into the field of retina.