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INTRODUCTION: To evaluate the long-term effectiveness and safety of XEN45 implant, either alone or in combination with cataract surgery, in patients with glaucoma. METHODS: Retrospective and single center study conducted on consecutive patients who underwent a XEN45 implant, either alone or in combination with cataract surgery, between November 2016 and October 2021. The primary endpoint was the mean IOP lowering from preoperative values. RESULTS: Among the 230 screened patients, 206 eyes (176 patients) were included. Fifty-three (25.7%) eyes had undergone XEN-alone and 153 (74.3%) eyes had undergone a combined procedure (XEN+Phacoemulsification). The mean preoperative intraocular pressure (IOP) was significantly higher in the XEN-alone (22.2±5.9 mmHg) than in the XEN+Phaco (19.8±4.5 mmHg) group (p=0.0035). In the overall study population, the mean preoperative IOP was significantly lowered from 20.5±5.0 mmHg to 15.8±4.4 at year-4, p<0.0001. The mean preoperative (95% CI) IOP was significantly lowered from 22.2 (20.6 to 23.8) mmHg and 19.8 (19.1 to 20.6) mmHg to 15.6 (12.2 to 16.9) mmHg and 15.9 (15.2 to 16.5) mmHg at year-4 in the XEN-alone and XEN+Phaco groups, respectively (p<0.0001 each, respectively). The number of ocular hypotensive medications was significant reduced from 2.6±1.0 drugs to 1.3±1.3 drugs, with no significant differences between XEN-alone and XEN+Phaco groups (p=0.1671). On the first postoperative day, 62 (30.1%) eyes presented some type of complication. Fifteen (7.3%) eyes underwent a needling procedure. CONCLUSION: XEN45, either alone or in combination with phacoemulsification, significantly lowered the IOP and reduce the need of ocular hypotensive medication in the long-term.
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PURPOSE: To evaluate alterations of the choroid in patients with a neurodegenerative disease versus healthy controls, a custom algorithm based on superpixel segmentation was used. DESIGN: A cross-sectional study was conducted on data obtained in a previous cohort study. SUBJECTS: Swept-source optical coherence tomography (OCT) B-scan images obtained using a Triton (Topcon, Japan) device were compiled according to current OSCAR IB and APOSTEL OCT image quality criteria. Images were included from three cohorts: multiple sclerosis (MS) patients, Parkinson disease (PD) patients, and healthy subjects. Only patients with early-stage MS and PD were included. METHODS: In total, 104 OCT B-scan images were processed using a custom superpixel segmentation (SpS) algorithm to detect boundary limits in the choroidal layer and the optical properties of the image. The algorithm groups pixels with similar structural properties to generate clusters with similar meaningful properties. MAIN OUTCOMES: SpS selects and groups the superpixels in a segmented choroidal area, computing the choroidal optical image density (COID), measured as the standard mean gray level, and the total choroidal area (CA), measured as px2. RESULTS: The CA and choroidal density (CD) were significantly reduced in the two neurodegenerative disease groups (higher in PD than in MS) versus the healthy subjects (p < 0.001); choroidal area was also significantly reduced in the MS group versus the healthy subjects. The COID increased significantly in the PD patients versus the MS patients and in the MS patients versus the healthy controls (p < 0.001). CONCLUSIONS: The SpS algorithm detected choroidal tissue boundary limits and differences optical density in MS and PD patients versus healthy controls. The application of the SpS algorithm to OCT images potentially acts as a non-invasive biomarker for the early diagnosis of MS and PD.
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Retinal Optical Coherence Tomography (OCT) allows the non-invasive direct observation of the central nervous system, enabling the measurement and extraction of biomarkers from neural tissue that can be helpful in the assessment of ocular, systemic and Neurological Disorders (ND). Deep learning models can be trained to segment the retinal layers for biomarker extraction. However, the onset of ND can have an impact on the neural tissue, which can lead to the degraded performance of models not exposed to images displaying signs of disease during training. We present a fully automatic approach for the retinal layer segmentation in multiple neurodegenerative disorder scenarios, using an annotated dataset of patients of the most prevalent NDs: Alzheimer's disease, Parkinson's disease, multiple sclerosis and essential tremor, along with healthy control patients. Furthermore, we present a two-part, comprehensive study on the effects of ND on the performance of these models. The results show that images of healthy patients may not be sufficient for the robust training of automated segmentation models intended for the analysis of ND patients, and that using images representative of different NDs can increase the model performance. These results indicate that the presence or absence of patients of ND in datasets should be taken into account when training deep learning models for retinal layer segmentation, and that the proposed approach can provide a valuable tool for the robust and reliable diagnosis in multiple scenarios of ND.
Subject(s)
Multiple Sclerosis , Parkinson Disease , Humans , Retina , Tomography, Optical Coherence/methodsABSTRACT
BACKGROUND: Current procedures for diagnosing multiple sclerosis (MS) present a series of limitations, making it critically important to identify new biomarkers. The aim of the study was to identify new biomarkers for the early diagnosis of MS using spectral-domain optical coherence tomography (OCT) and artificial intelligence. METHODS: Spectral domain OCT was performed on 79 patients with relapsing-remitting multiple sclerosis (RRMS) (disease duration ≤ 2 years, no history of optic neuritis) and on 69 age-matched healthy controls using the posterior pole protocol that incorporates the anatomic Positioning System. Median retinal thickness values in both eyes and inter-eye difference in healthy controls and patients were evaluated by area under the receiver operating characteristic (AUROC) curve analysis in the foveal, parafoveal and perifoveal areas and in the overall area spanned by the three rings. The structures with the greatest discriminant capacity - retinal thickness and inter-eye difference - were used as inputs to a convolutional neural network to assess the diagnostic capability. RESULTS: Analysis of retinal thickness and inter-eye difference in RRMS patients revealed that greatest alteration occurred in the ganglion cell (GCL), inner plexiform (IPL), and inner retinal (IRL) layers. By using the average thickness of the GCL (AUROC = 0.82) and the inter-eye difference in the IPL (AUROC = 0.71) as inputs to a two-layer convolutional neural network, automatic diagnosis attained accuracy = 0.87, sensitivity = 0.82, and specificity = 0.92. CONCLUSION: This study adds weight to the argument that neuroretinal structure analysis could be incorporated into the diagnostic criteria for MS.
