4 research outputs found

    A novel mutation in the Choroideremia gene in a Turkish family.

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    Choroideremia is an X-linked recessive genetic disorder caused by mutations in the CHM gene. It is a rare retinal dystrophy that manifests as nyctalopia and vision loss, progressing to blindness in later stages. We report a 21-year Turkish man who presented with nyctalopia for the past 4-5 years. His mother and maternal grandmother had similar, but less pronounced complaints. Fundus examination revealed pigmentary changes and retinal atrophy in both eyes. Optical coherence tomography showed outer retinal loss, with central island of preserved autofluorescence surrounded by absent autofluorescence on fundus autofluorescence examination. Goldmann visual fields were constricted. Microperimetry detected retinal sensitivity losses, and full-field electroretinogram demonstrated extinguished cone responses. Genetic analysis revealed a novel nonsense mutation in the CHM gene, namely p.E480X: c.1438G \u3eT. The mutation causes a premature stop codon in exon 12. This is the first report of a G1438T mutation resulting in an E480X premature stop in the CHM gene

    Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.

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    PURPOSE: To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data. METHODS: In this phase IV, retrospective, proof of concept, single center study, SS-OCT data from 17 previously treated nARMD eyes was used to assess retinal layer thicknesses, as well as quantify intraretinal fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs) using a novel deep learning-based, macular fluid segmentation algorithm. Baseline OCT and OCT-A morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA from baseline to week 52. RESULTS: Total retinal fluid (IRF, SRF and PED) volume at baseline had the strongest correlation to improvement in BCVA at month 12 (PCC = 0.652, p = 0.005). Fluid was subsequently sub-categorized into IRF, SRF and PED, with PED volume having the next highest correlation (PCC = 0.648, p = 0.005) to BCVA improvement. Average total retinal thickness in isolation demonstrated poor correlation (PCC = 0.334, p = 0.189). When two features, mean choroidal neovascular membranes (CNVM) size and total fluid volume, were combined and correlated with visual outcomes, the highest correlation increased to PCC = 0.695 (p = 0.002). CONCLUSIONS: In isolation, total fluid volume most closely correlates with change in BCVA values between baseline and week 52. In combination with complimentary information from OCT-A, an improvement in the linear correlation score was observed. Average total retinal thickness provided a lower correlation, and thus provides a lower predictive outcome than alternative metrics assessed. Clinically, a machine-learning approach to analyzing fluid metrics in combination with lesion size may provide an advantage in personalizing therapy and predicting BCVA outcomes at week 52
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