BackgroundTo describe the spectral domain optical coherence tomography (SD-OCT) findings of a patient who developed pigmentary retinopathy following high-dose deferoxamine administration.Case presentationA 34-year-old man with thalassemia major complained of nyctalopia and decreased vision following high-dose intravenous deferoxamine to treat systemic iron overload. Fundus examination revealed multiple discrete hypo-pigmented lesions at the posterior pole and mid-peripheral retina. Recovery was partial following cessation of desferrioxamine six weeks later. A follow-up SD-OCT showed multiple accumulated hyper-reflective deposits primarily in the choroid, retina pigment epithelium (RPE), and inner segment and outer segment (IS/OS) junction.ConclusionDeferoxamine retinopathy primarily targets the RPE–Bruch membrane–photoreceptor complex, extending from the peri-fovea to the peripheral retina with foveola sparing. An SD-OCT examination can serve as a simple, noninvasive tool for early detection and long-term follow-up.
Background Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. Objective The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. Methods This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. Results A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. Conclusions Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.
Purpose. To compare the effects of early phacoemulsification and intraocular lens implantation (phaco/IOL), delayed phaco/IOL after initial laser peripheral iridotomy (LPI), and conventional LPI alone in patients with acute primary angle-closure (PAC). Methods. Patients with acute PAC were included in the study, and those with secondary glaucoma, prior ocular trauma, or other ocular diseases and those who had undergone ocular surgeries previously were excluded. Patients were categorized into three groups: Group A, which underwent primary phaco/IOL after acute PAC; Group B, which underwent LPI initially after acute PAC, followed by phaco/IOL within 6 months; and Group C, which underwent LPI alone. The IOP control success at 12 months as well as changes in ocular characteristics and the number of antiglaucoma medications used after the treatment among the groups were evaluated. Results. Eighty-one eyes were included in the study: 24 eyes in Group A, 23 eyes in Group B, and 34 eyes in Group C. The linear mixed model analysis demonstrated considerable IOP control in Groups A and B. Visual acuity, anterior chamber depth (ACD), and angle width improved significantly in Groups A and B, but not in Group C. The number of antiglaucoma medications used was significantly higher in Group C than in Groups A and B. Conclusions. Patients who underwent phaco/IOL had better IOP control, improved vision, deeper ACD, and wider angle and required less antiglaucoma medications than those who underwent LPI alone. Performing phaco/IOL weeks to months after the initial LPI did not appear to adversely affect outcomes compared with those of early phaco/IOL.
In this retrospective, multicenter study, we determined the predictive value of imaging biomarkers in diabetic macular edema (DME) outcomes following dexamethasone (DEX) implant(s). Sixty-seven eyes of 47 patients’ best-corrected visual acuity (BCVA) and central foveal thickness (CFT) on optical coherence tomography (OCT) before and after intravitreal DEX implants were evaluated. Baseline imaging biomarkers were graded using fundus photography and OCT, and the predictive value of biomarkers for significant treatment effects at six months was analyzed. Six months after 2.0 ± 0.8 (mean ± SD) DEX implants, 35 (52%) and 16 (24%) eyes had CFT reduction ≥ 10% from baseline and decreased to < 300 µm, respectively. BCVA improved ≥ 3 lines in 15 (22%) and remained stable in 38 (57%) eyes. At six months, eyes with severe intraretinal cyst (IRC), abundant hyperreflective dots (HRD), and moderate or severe hard exudate had a significantly higher chance of CFT reduction ≥ 10%. Eyes with abundant HRD at baseline and those underwent three DEX implants were more likely to achieve CFT < 300 µm. Eyes with DME and severe IRC, abundant HRD, or moderate-to-severe hard exudate at baseline were more likely to show a significant reduction in CFT six months after DEX implant.
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