Comparison of Normal- and High-Tension Glaucoma: Nerve Fiber Layer and Optic Nerve Head Damage

Purpose: The aim of this study was to investigate differences in the nerve fiber layer and glaucoma-induced structural optic nerve head (ONH) damage in patients with normal- (NTG) and high-tension (HTG) glaucoma. Methods: In this retrospective pair-matched comparative study, 22 NTG and 22 HTG eyes were matched according to the same glaucomatous damage based on rim volume, rim area and disk size, as measured by Heidelberg retinal tomography (HRT III). Visual fields (VF) were assessed by Humphrey perimetry, and nerve fiber layer thickness was determined both by scanning laser polarimetry (GDxVCC) and spectral-domain optical coherence tomography (SD-OCT). Comparisons of all measured parameters were made between NTG and HTG groups. Results: Based on HRT results, both NTG and HTG eyes displayed comparable structural damage to the ONH (NTG/HTG, mean: disk area, 2.30/2.31 mm 2 , p = 0.942; rim area, 1.02/0.86 mm 2 , p = 0.082; rim volume, 0.19/0.17 mm 3 , p = 0.398). NTG eyes had significantly less VF damage than HTG eyes (NTG/HTG, mean deviation: –4.23/–12.12 dB, p = 0.002; pattern standard deviation: 5.39/8.23 dB, p = 0.022). The inferior nerve fiber layer of NTG patients was significantly thicker than that of HTG patients (NTG/HTG, mean: GDx inferior: 53.5/46.3 μm, p = 0.046). SD-OCT revealed a significantly thicker nerve fiber in NTG compared with HTG patients in all quadrants (NTG/HTG, total mean: 72.72/58.45 μm, p = 0.002). Conclusion: At comparable glaucomatous stages, nerve fiber loss was more advanced in HTG patients compared with NTG patients

Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System

In ophthalmology, intravitreal operative medication therapy (IVOM) is widespread treatment for diseases such as the age-related macular degeneration (AMD), the diabetic macular edema (DME), as well as the retinal vein occlusion (RVO). However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. In this contribution, we present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital. The extensive data corpus allows predictive statements of the expected progression of a patient and his or her VA in each of the three diseases. Within our proposed multistage system, we classify the VA progression into the three groups of therapy "winners", "stabilizers", and "losers" (WSL scheme). Our OCT biomarker classification using an ensemble of deep neural networks results in a classification accuracy (F1-score) of over 98 %, enabling us to complete incomplete OCT documentations while allowing us to exploit them for a more precise VA modelling process. Our VA prediction requires at least four VA examinations and optionally OCT biomarkers from the same time period to predict the VA progression within a forecasted time frame. While achieving a prediction accuracy of up to 69 % (macro average F1-score) when considering all three WSL-based progression groups, this corresponds to an improvement by 11 % in comparison to our ophthalmic expertise (58 %).Comment: Work in progress Scientific Reports preprin