[Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration]. / Einsatz von künstlicher Intelligenz zur Erkennung von Biomarkern bei der intermediären altersabhängigen Makuladegeneration.

Advances in imaging and artificial intelligence (AI) have revolutionized the detection, quantification and monitoring for the clinical assessment of intermediate age-related macular degeneration (iAMD). The iAMD incorporates a broad spectrum of manifestations, which range from individual small drusen, hyperpigmentation, hypopigmentation up to early stages of geographical atrophy. Current high-resolution imaging technologies enable an accurate detection and description of anatomical features, such as drusen volumes, hyperreflexive foci and photoreceptor degeneration, which are risk factors that are decisive for prediction of the course of the disease; however, the manual annotation of these features in complex optical coherence tomography (OCT) scans is impractical for the routine clinical practice and research. In this context AI provides a solution by fully automatic segmentation and therefore delivers exact, reproducible and quantitative analyses of AMD-related biomarkers. Furthermore, the application of AI in iAMD facilitates the risk assessment and the development of structural endpoints for new forms of treatment. For example, the quantitative analysis of drusen volume and hyperreflective foci with AI algorithms has shown a correlation with the progression of the disease. These technological advances therefore improve not only the diagnostic precision but also support future targeted treatment strategies and contribute to the prioritized target of personalized medicine in the diagnostics and treatment of AMD.

Accurate drusen segmentation in optical coherence tomography via order-constrained regression of retinal layer heights.

Drusen are an important biomarker for age-related macular degeneration (AMD). Their accurate segmentation based on optical coherence tomography (OCT) is therefore relevant to the detection, staging, and treatment of disease. Since manual OCT segmentation is resource-consuming and has low reproducibility, automatic techniques are required. In this work, we introduce a novel deep learning based architecture that directly predicts the position of layers in OCT and guarantees their correct order, achieving state-of-the-art results for retinal layer segmentation. In particular, the average absolute distance between our model's prediction and the ground truth layer segmentation in an AMD dataset is 0.63, 0.85, and 0.44 pixel for Bruch's membrane (BM), retinal pigment epithelium (RPE) and ellipsoid zone (EZ), respectively. Based on layer positions, we further quantify drusen load with excellent accuracy, achieving 0.994 and 0.988 Pearson correlation between drusen volumes estimated by our method and two human readers, and increasing the Dice score to 0.71 ± 0.16 (from 0.60 ± 0.23) and 0.62 ± 0.23 (from 0.53 ± 0.25), respectively, compared to a previous state-of-the-art method. Given its reproducible, accurate, and scalable results, our method can be used for the large-scale analysis of OCT data.

Reliability of Retinal Layer Annotation with a Novel, High-Resolution Optical Coherence Tomography Device: A Comparative Study.

Optical coherence tomography (OCT) enables in vivo diagnostics of individual retinal layers in the living human eye. However, improved imaging resolution could aid diagnosis and monitoring of retinal diseases and identify potential new imaging biomarkers. The investigational high-resolution OCT platform (High-Res OCT; 853 nm central wavelength, 3 µm axial-resolution) has an improved axial resolution by shifting the central wavelength and increasing the light source bandwidth compared to a conventional OCT device (880 nm central wavelength, 7 µm axial-resolution). To assess the possible benefit of a higher resolution, we compared the retest reliability of retinal layer annotation from conventional and High-Res OCT, evaluated the use of High-Res OCT in patients with age-related macular degeneration (AMD), and assessed differences of both devices on subjective image quality. Thirty eyes of 30 patients with early/intermediate AMD (iAMD; mean age 75 ± 8 years) and 30 eyes of 30 age-similar subjects without macular changes (62 ± 17 years) underwent identical OCT imaging on both devices. Inter- and intra-reader reliability were analyzed for manual retinal layer annotation using EyeLab. Central OCT B-scans were graded for image quality by two graders and a mean-opinion-score (MOS) was formed and evaluated. Inter- and intra-reader reliability were higher for High-Res OCT (greatest benefit for inter-reader reliability: ganglion cell layer; for intra-reader reliability: retinal nerve fiber layer). High-Res OCT was significantly associated with an improved MOS (MOS 9/8, Z-value = 5.4, p < 0.01) mainly due to improved subjective resolution (9/7, Z-Value 6.2, p < 0.01). The retinal pigment epithelium drusen complex showed a trend towards improved retest reliability in High-Res OCT in iAMD eyes but without statistical significance. Improved axial resolution of the High-Res OCT benefits retest reliability of retinal layer annotation and improves perceived image quality and resolution. Automated image analysis algorithms could also benefit from the increased image resolution.

Comparability of automated drusen volume measurements in age-related macular degeneration: a MACUSTAR study report.

Drusen are hallmarks of early and intermediate age-related macular degeneration (AMD) but their quantification remains a challenge. We compared automated drusen volume measurements between different OCT devices. We included 380 eyes from 200 individuals with bilateral intermediate (iAMD, n = 126), early (eAMD, n = 25) or no AMD (n = 49) from the MACUSTAR study. We assessed OCT scans from Cirrus (200 × 200 macular cube, 6 × 6 mm; Zeiss Meditec, CA) and Spectralis (20° × 20°, 25 B-scans; 30° × 25°, 241 B-scans; Heidelberg Engineering, Germany) devices. Sensitivity and specificity for drusen detection and differences between modalities were assessed with intra-class correlation coefficients (ICCs) and mean difference in a 5 mm diameter fovea-centered circle. Specificity was > 90% in the three modalities. In eAMD, we observed highest sensitivity in the denser Spectralis scan (68.1). The two different Spectralis modalities showed a significantly higher agreement in quantifying drusen volume in iAMD (ICC 0.993 [0.991-0.994]) than the dense Spectralis with Cirrus scan (ICC 0.807 [0.757-0.847]). Formulae for drusen volume conversion in iAMD between the two devices are provided. Automated drusen volume measures are not interchangeable between devices and softwares and need to be interpreted with the used imaging devices and software in mind. Accounting for systematic difference between methods increases comparability and conversion formulae are provided. Less dense scans did not affect drusen volume measurements in iAMD but decreased sensitivity for medium drusen in eAMD.Trial registration: ClinicalTrials.gov NCT03349801. Registered on 22 November 2017.

[Multimodal imaging and evaluation in the age of artificial intelligence]. / Multimodale Bildgebung und -auswertung im Zeitalter von künstlicher Intelligenz.

Multimodal imaging is able to image the retina in unprecedented detail, and the joint analysis (integration) of these data not only enables the securing of diagnoses, but also a more precise definition; however, humans encounter temporal and cognitive limitations in the analysis of this amount of information, so that the potential of a joint examination of the findings is largely unused to date. Automatic image processing and methods, which are summarized under the collective term of artificial intelligence (AI), are able to overcome the bottleneck in the evaluation and to exploit the full potential of the available data. A basic understanding of AI methods and the ability to implement them will become increasingly more important for ophthalmologists in the future. In this article we give an insight into the functionality of AI methods and the current state of research in the field of automatic image analysis.

Extending the Time Domain of Neuronal Silencing with Cryptophyte Anion Channelrhodopsins.

Optogenetic inhibition of specific neuronal types in the brain enables analysis of neural circuitry and is promising for the treatment of a number of neurological disorders. Anion channelrhodopsins (ACRs) from the cryptophyte alga Guillardia theta generate larger photocurrents than other available inhibitory optogenetic tools, but more rapid channels are needed for temporally precise inhibition, such as single-spike suppression, of high-frequency firing neurons. Faster ACRs have been reported, but their potential advantages for time-resolved inhibitory optogenetics have not so far been verified in neurons. We report RapACR, nicknamed so for "rapid," an ACR from Rhodomonas salina, that exhibits channel half-closing times below 10 ms and achieves equivalent inhibition at 50-fold lower light intensity in lentivirally transduced cultured mouse hippocampal neurons as the second-generation engineered Cl--conducting channelrhodopsin iC++. The upper limit of the time resolution of neuronal silencing with RapACR determined by measuring the dependence of spiking recovery after photoinhibition on the light intensity was calculated to be 100 Hz, whereas that with the faster of the two G. theta ACRs was 13 Hz. Further acceleration of RapACR channel kinetics was achieved by site-directed mutagenesis of a single residue in transmembrane helix 3 (Thr111 to Cys). We also show that mutation of another ACR (Cys to Ala at the same position) with a greatly extended lifetime of the channel open state acts as a bistable photochromic tool in mammalian neurons. These molecules extend the time domain of optogenetic neuronal silencing while retaining the high light sensitivity of Guillardia ACRs.

The Expanding Family of Natural Anion Channelrhodopsins Reveals Large Variations in Kinetics, Conductance, and Spectral Sensitivity.

Natural anion channelrhodopsins (ACRs) discovered in the cryptophyte alga Guillardia theta generate large hyperpolarizing currents at membrane potentials above the Nernst equilibrium potential for Cl- and thus can be used as efficient inhibitory tools for optogenetics. We have identified and characterized new ACR homologs in different cryptophyte species, showing that all of them are anion-selective, and thus expanded this protein family to 20 functionally confirmed members. Sequence comparison of natural ACRs and engineered Cl--conducting mutants of cation channelrhodopsins (CCRs) showed radical differences in their anion selectivity filters. In particular, the Glu90 residue in channelrhodopsin 2, which needed to be mutated to a neutral or alkaline residue to confer anion selectivity to CCRs, is nevertheless conserved in all of the ACRs identified. The new ACRs showed a large variation of the amplitude, kinetics, and spectral sensitivity of their photocurrents. A notable variant, designated "ZipACR", is particularly promising for inhibitory optogenetics because of its combination of larger current amplitudes than those of previously reported ACRs and an unprecedentedly fast conductance cycle (current half-decay time 2-4 ms depending on voltage). ZipACR expressed in cultured mouse hippocampal neurons enabled precise photoinhibition of individual spikes in trains of up to 50 Hz frequency.