1,656 research outputs found

    Towards modifying the genetic predisposition for glaucoma: An overview of the contribution and interaction of genetic and environmental factors

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    Glaucoma, the leading cause of irreversible blindness worldwide, is a complex human disease, with both genetic and environmental determinants. The availability of large-scale, population-based cohorts and biobanks, combining genotyping and detailed phenotyping, has greatly accelerated research into the aetiology of glaucoma in recent years. Hypothesis-free genome-wide association studies have furthered our understanding of the complex genetic architecture underpinning the disease, while epidemiological studies have provided advances in the identification and characterisation of environmental risk factors. It is increasingly recognised that the combined effects of genetic and environmental factors may confer a disease risk that reflects a departure from the simple additive effect of the two. These gene-environment interactions have been implicated in a host of complex human diseases, including glaucoma, and have several important diagnostic and therapeutic implications for future clinical practice. Importantly, the ability to modify the risk associated with a particular genetic makeup promises to lead to personalised recommendations for glaucoma prevention, as well as novel treatment approaches in years to come. Here we provide an overview of genetic and environmental risk factors for glaucoma, as well as reviewing the evidence and discussing the implications of gene-environment interactions for the disease

    Reliable intraocular pressure measurement using automated radio-wave telemetry

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    Purpose To present an autonomous intraocular pressure (IOP) measurement technique using a wireless implantable transducer (WIT) and a motion sensor. Methods: The WIT optical aid was implanted within the ciliary sulcus of a normotensive rabbit eye after extracapsular clear lens extraction. An autonomous wireless data system (AWDS) comprising of a WIT and an external antenna aided by a motion sensor provided continuous IOP readings. The sensitivity of the technique was determined by the ability to detect IOP changes resulting from the administration of latanoprost 0.005% or dorzolamide 2%, while the reliability was determined by the agreement between baseline and vehicle (saline) IOP. Results: On average, 12 diurnal and 205 nocturnal IOP measurements were performed with latanoprost, and 26 diurnal and 205 nocturnal measurements with dorzolamide. No difference was found between mean baseline IOP (13.08±2.2 mmHg) and mean vehicle IOP (13.27±2.1 mmHg) (P=0.45), suggesting good measurement reliability. Both antiglaucoma medications caused significant IOP reduction compared to baseline; latanoprost reduced mean IOP by 10% (1.3±3.54 mmHg; P<0.001), and dorzolamide by 5% (0.62±2.22 mmHg; P<0.001). Use of latanoprost resulted in an overall twofold higher IOP reduction compared to dorzolamide (P<0.001). Repeatability was ±1.8 mmHg, assessed by the variability of consecutive IOP measurements performed in a short period of time (≤1 minute), during which the IOP is not expected to change. Conclusion: IOP measurements in conscious rabbits obtained without the need for human interactions using the AWDS are feasible and provide reproducible results

    Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization

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    Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are available, though minority groups suffer from more health issues. To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection. Glaucoma is the leading cause of irreversible blindness globally with Blacks having doubled glaucoma prevalence than other races. We also propose a fair identity normalization (FIN) approach to equalize the feature importance between different identity groups. Our FIN approach is compared with various the-state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of our dataset Harvard-GF for fairness learning. To facilitate fairness comparisons between different models, we propose an equity-scaled performance measure, which can be flexibly used to compare all kinds of performance metrics in the context of fairness. The dataset and code are publicly accessible via \url{https://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/}

    An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

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    Purpose: To detect visual field (VF) progression by analyzing spatial pattern changes. Methods: We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists. Results: In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P \u3c 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60). Conclusions: The archetype method can inform clinicians of VF progression patterns
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