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Three dimensional neuro-retinal rim thickness and retinal nerve fiber layer thickness using high-definition optical coherence tomography for open-angle glaucoma

Subramaniam S, Jeoung JW, Lee WJ, Kim YK, Park KH

Jpn. J. Ophthalmol., 2018 Nov;62(6):634-642.
PMID: 30229404 DOI: 10.1007/s10384-018-0620-7

PURPOSE: To compare the diagnostic capability of three-dimensional (3D) neuro-retinal rim thickness (NRR) with existing optic nerve head and retinal nerve fiber layer (RNFL) scan parameters using high-definition optical coherence tomography (HD-OCT).
DESIGN: Retrospective study.
METHODS: Based on the mean deviation (MD) of the Humphrey Field Analyzer (HFA), the 152 subjects were categorized into mild (MD > - 6 dB, 100), moderate (MD - 6 to - 12 dB, 26), and severe (MD
Fulltext Green Silver Nanoparticles Formed by Phyllanthus urinaria, Pouzolzia zeylanica, and Scoparia dulcis Leaf Extracts and the Antifungal Activity

Nguyen DH, Lee JS, Park KD, Ching YC, Nguyen XT, Phan VHG, et al.

Nanomaterials (Basel), 2020 Mar 17;10(3).
PMID: 32192177 DOI: 10.3390/nano10030542

Phytoconstituents presenting in herbal plant broths are the biocompatible, regenerative, and cost-effective sources that can be utilized for green synthesis of silver nanoparticles. Different plant extracts can form nanoparticles with specific sizes, shapes, and properties. In the study, we prepared silver nanoparticles (P.uri.AgNPs, P.zey.AgNPs, and S.dul.AgNPs) based on three kinds of leaf extracts (Phyllanthus urinaria, Pouzolzia zeylanica, and Scoparia dulcis, respectively) and demonstrated the antifungal capacity. The silver nanoparticles were simply formed by adding silver nitrate to leaf extracts without using any reducing agents or stabilizers. Formation and physicochemical properties of these silver nanoparticles were characterized by UV-vis, Fourier transforms infrared spectroscopy, scanning electron microscope, transmission electron microscope, and energy dispersive X-ray spectroscopy. P.uri.AgNPs were 28.3 nm and spherical. P.zey.AgNPs were 26.7 nm with hexagon or triangle morphologies. Spherical S.dul.AgNPs were formed and they were relatively smaller than others. P.uri.AgNPs, P.zey.AgNPs and S.dul.AgNPs exhibited the antifungal ability effective against Aspergillus niger, Aspergillus flavus, and Fusarium oxysporum, demonstrating their potentials as fungicides in the biomedical and agricultural applications.
Development of the clinical assessment scale in autoimmune encephalitis

Lim JA, Lee ST, Moon J, Jun JS, Kim TJ, Shin YW, et al.

Ann Neurol, 2019 03;85(3):352-358.
PMID: 30675918 DOI: 10.1002/ana.25421

OBJECTIVE: There is no scale for rating the severity of autoimmune encephalitis (AE). In this study, we aimed to develop a novel scale for rating severity in patients with diverse AE syndromes and to verify the reliability and validity of the developed scale.
METHODS: The key items were generated by a panel of experts and selected according to content validity ratios. The developed scale was initially applied to 50 patients with AE (development cohort) to evaluate its acceptability, reproducibility, internal consistency, and construct validity. Then, the scale was applied to another independent cohort (validation cohort, n = 38).
RESULTS: A new scale consisting of 9 items (seizure, memory dysfunction, psychiatric symptoms, consciousness, language problems, dyskinesia/dystonia, gait instability and ataxia, brainstem dysfunction, and weakness) was developed. Each item was assigned a value of up to 3 points. The total score could therefore range from 0 to 27. We named the scale the Clinical Assessment Scale in Autoimmune Encephalitis (CASE). The new scale showed excellent interobserver (intraclass correlation coefficient [ICC] = 0.97) and intraobserver (ICC = 0.96) reliability for total scores, was highly correlated with modified Rankin scale (r = 0.86, p
Fulltext Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Gunasekeran DV, Zheng F, Lim GYS, Chong CCY, Zhang S, Ng WY, et al.

Front Med (Lausanne), 2022;9:875242.
PMID: 36314006 DOI: 10.3389/fmed.2022.875242

BACKGROUND: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.
METHODS: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.
RESULTS: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83.
CONCLUSION: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.
Genome-wide association study identifies five new susceptibility loci for primary angle closure glaucoma

Khor CC, Do T, Jia H, Nakano M, George R, Abu-Amero K, et al.

Nat Genet, 2016 May;48(5):556-62.
PMID: 27064256 DOI: 10.1038/ng.3540

Primary angle closure glaucoma (PACG) is a major cause of blindness worldwide. We conducted a genome-wide association study (GWAS) followed by replication in a combined total of 10,503 PACG cases and 29,567 controls drawn from 24 countries across Asia, Australia, Europe, North America, and South America. We observed significant evidence of disease association at five new genetic loci upon meta-analysis of all patient collections. These loci are at EPDR1 rs3816415 (odds ratio (OR) = 1.24, P = 5.94 × 10(-15)), CHAT rs1258267 (OR = 1.22, P = 2.85 × 10(-16)), GLIS3 rs736893 (OR = 1.18, P = 1.43 × 10(-14)), FERMT2 rs7494379 (OR = 1.14, P = 3.43 × 10(-11)), and DPM2-FAM102A rs3739821 (OR = 1.15, P = 8.32 × 10(-12)). We also confirmed significant association at three previously described loci (P < 5 × 10(-8) for each sentinel SNP at PLEKHA7, COL11A1, and PCMTD1-ST18), providing new insights into the biology of PACG.