[Screening and management of retinal diseases using digital medicine]. / Screening und Management retinaler Erkrankungen mittels digitaler Medizin.

BACKGROUND: Modern retinal imaging creates gigantic amounts of data (big data) of anatomic information. At the same time patient numbers and interventions are increasing exponentially. OBJECTIVE: Introduction of artificial intelligence (AI) for optimization of personalized therapy and diagnosis. MATERIAL AND METHODS: Deep learning was introduced for automated segmentation and recognition of risk factors and activity levels in retinal diseases. RESULTS: Automated algorithms enable the precise identification and quantification of retinal fluid in all compartments. Early detection of retinopathy in diabetes or glaucoma or risk determination for the development of age-related macular degeneration (AMD) are possible as well as an individual visual prognosis and evaluation of the need for retreatment in intravitreal injection therapy. CONCLUSION: Methods using AI constitute a breakthrough perspective for the introduction of individualized medicine and optimization of diagnosis and therapy, screening and prognosis.

Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.

PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five steps for binary retinal fluid segmentation. The method is based on feature extraction, pre-segmention, dimension reduction procedures, and supervised learning tools.ResultsFluid identification using our pipeline was tested on two separate patient groups: one associated to neovascular age-related macular degeneration, the other showing diabetic macular edema. For training and evaluation purposes, retinal fluid was annotated manually in each cross-section by human expert graders of the Vienna Reading Center. Compared with the manual annotations, our pipeline yields good quantification, visually and in numbers.ConclusionsBy demonstrating good automated retinal fluid quantification, our pipeline appears useful to expert graders within their current grading processes. Owing to dimension reduction, the actual learning part is fast and requires only few training samples. Hence, it is well-suited for integration into actual manufacturer's devices, further improving segmentation by its use in daily clinical life.

A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration.

Optical coherence tomography (OCT) has become an established diagnostic technology in the clinical management of age-related macular degeneration (AMD). OCT is being used for primary diagnosis, evaluation of therapeutic efficacy, and long-term monitoring. Computer-based advances in image analysis provide complementary imaging tools such as OCT angiography, further novel automated analysis methods as well as feature detection and prediction of prognosis in disease and therapy by machine learning. In early AMD, pathognomonic features such as drusen, pseudodrusen, and abnormalities of the retinal pigment epithelium (RPE) can be imaged in a qualitative and quantitative way to identify early signs of disease activity and define the risk of progression. In advanced AMD, disease activity can be monitored clearly by qualitative and quantified analyses of fluid pooling, such as intraretinal cystoid fluid, subretinal fluid, and pigment epithelial detachment (PED). Moreover, machine learning methods detect a large spectrum of new biomarkers. Evaluation of treatment efficacy and definition of optimal therapeutic regimens are an important aim in managing neovascular AMD. In atrophic AMD hallmarked by geographic atrophy (GA), advanced spectral domain (SD)-OCT imaging largely replaces conventional fundus autofluorescence (FAF) as it adds insight into the condition of the neurosensory layers and associated alterations at the level of the RPE and choroid. Exploration of imaging features by computerized methods has just begun but has already opened relevant and reliable horizons for the optimal use of OCT imaging for individualized and population-based management of AMD-the leading retinal epidemic of modern times.

Comparison of penetration depth in choroidal imaging using swept source vs spectral domain optical coherence tomography.

PURPOSE: To compare signal penetration depth and deep structure-visualization of swept source (SS) and spectral domain (SD)-optical coherence tomography (OCT) with and without enhanced depth imaging (EDI) and B-scan averaging modes. METHODS: Volume scans were obtained from 20 eyes of healthy volunteers by DRI OCT-1, Spectralis using EDI and B-scan averaging, and Cirrus HD-OCT. The signal penetration depth was measured as the distance between the retinal pigment epithelium and the deepest visible anatomical structure at the foveal center. Visibility and contrast of the choroidoscleral junction and of vascular details within the choroid were assessed across the entire volume using an ordinal scoring scale. Outcome measures were compared using paired t-test and rank-sum test. RESULTS: The mean signal penetration depth was 498±114 µm for Spectralis, 491±85 µm for DRI OCT-1, and 123±65 µm for Cirrus; P=0.9708 Spectralis vs DRI OCT-1, P<0.0001 Spectralis vs Cirrus, and P<0.0001 DRI OCT-1 vs Cirrus. Mean ranks for visibility and contrast of the choroidoscleral junction were 3.83 for Spectralis, 3.98 for DRI OCT-1, and 2.00 for Cirrus; and 3.45 for Spectralis, 2.93 for DRI OCT-1, and 1.58 for Cirrus. Mean ranks for visibility and contrast of vascular details were 3.73 (Spectralis), 3.70 (DRI OCT-1), and 2.23 (Cirrus); and 3.53 (Spectralis), 2.05 (DRI OCT-1), and 1.98 (Cirrus). CONCLUSION: Signal penetration depths are similar for SS-OCT and SD-OCT using EDI and frame averaging, and statistically significantly lower without EDI/averaging. Both SD-OCT using EDI/frame averaging and SS-OCT offer excellent visualization capabilities for volumetric imaging of the choroidoscleral interface.

Two-wavelength fundus autofluorescence and macular pigment optical density imaging in diabetic macular oedema.

PURPOSE: To evaluate the application of 488 and 514 nm fundus autofluorescence (FAF) and macular pigment optical density (MPOD) imaging in diabetic macular oedema (DMO) and to demonstrate the typical imaging features. PATIENTS AND METHODS: A hundred and twenty-five eyes of 71 consecutive patients with diabetic retinopathy who underwent examination at a specialist university clinic employing a modified Heidelberg Retina Angiograph, using two different light sources of 488 and 514 nm wavelength, were retrospectively reviewed. MPOD images were calculated using modified Heidelberg Eye Explorer software. All images were evaluated by two independent masked graders. Features from FAF and MPOD images were correlated with optical coherence tomography (OCT) imaging findings and inter-grader variability, sensitivity and specificity were calculated using OCT as reference. RESULTS: Sixty-seven eyes had DMO on OCT. The inter-grader variability was 0.84 for 488 nm FAF, 0.63 for 514 nm FAF and 0.79 for MPOD imaging. Sensitivity and specificity for detection of DMO were 80.6 and 89.7% for 488 nm FAF; 55.2 and 94.8% for 514 nm FAF; and 80.6 and 91.4% for MPOD imaging. In 488 nm FAF and MPOD imaging, DMO was better visualised in comparison with 514 nm FAF imaging, P<0.01. MPOD revealed displacement of macular pigment by intraretinal cysts. CONCLUSION: MPOD imaging, and particularly its combination with 488 nm and 514 nm FAF, provides a valuable addition to OCT in the evaluation of DMO and is clinically useful in rapid en-face assessment of the central macula.