A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data

Stereo Photo Measured ONH Shape Predicts Development of POAG in Subjects With Ocular Hypertension

To identify objective, quantitative optic nerve head (ONH) structural features and model the contributions of glaucoma

Deep learning-based improvement for the outcomes of glaucoma clinical trials

Glaucoma is the leading cause of irreversible blindness worldwide. It is a progressive optic neuropathy in which retinal ganglion cell (RGC) axon loss, probably as a consequence of damage at the optic disc, causes a loss of vision, predominantly affecting the mid-peripheral visual field (VF). Glaucoma results in a decrease in vision-related quality of life and, therefore, early detection and evaluation of disease progression rates is crucial in order to assess the risk of functional impairment and to establish sound treatment strategies. The aim of my research is to improve glaucoma diagnosis by enhancing state of the art analyses of glaucoma clinical trial outcomes using advanced analytical methods. This knowledge would also help better design and analyse clinical trials, providing evidence for re-evaluating existing medications, facilitating diagnosis and suggesting novel disease management. To facilitate my objective methodology, this thesis provides the following contributions: (i) I developed deep learning-based super-resolution (SR) techniques for optical coherence tomography (OCT) image enhancement and demonstrated that using super-resolved images improves the statistical power of clinical trials, (ii) I developed a deep learning algorithm for segmentation of retinal OCT images, showing that the methodology consistently produces more accurate segmentations than state-of-the-art networks, (iii) I developed a deep learning framework for refining the relationship between structural and functional measurements and demonstrated that the mapping is significantly improved over previous techniques, iv) I developed a probabilistic method and demonstrated that glaucomatous disc haemorrhages are influenced by a possible systemic factor that makes both eyes bleed simultaneously. v) I recalculated VF slopes, using the retinal never fiber layer thickness (RNFLT) from the super-resolved OCT as a Bayesian prior and demonstrated that use of VF rates with the Bayesian prior as the outcome measure leads to a reduction in the sample size required to distinguish treatment arms in a clinical trial

A fruitful fly forward : the role of the fly in drug discovery for neurodegeneration

AD, Alzheimer’s disease; APP, amyloid precursor protein; BBB, blood brain barrier; GFP, green fluorescent protein; HTS, high-throughput screening; HD, Huntington’s disease; LB, Lewy bodies; PD, Parkinson’s disease; PolyQ, Polyglutamine; RNAi, RNA interference; SNCA, α-synuclein gene; UAS, Upstream Activating Sequence.peer-reviewe

한국인 코호트에서 원발 개방각녹내장 연관 새로운 유전자 변이 발굴 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의학과, 2020. 8. 박기호.Primary open-angle glaucoma (POAG) can develop even within normal ranges of intraocular pressure, and this type of glaucoma (so-called normal-tension glaucoma [NTG]) is highly prevalent in East Asia including Korea and Japan. We conducted exome chip analysis to identify low-frequency and rare variants associated with POAG from the primary cohort (309 POAG patients and 5,400 control, all Koreans). For replication, Korean (310 POAG patients and 5,612 controls) and Japanese (565 POAG patients and 1,104 controls) cohorts were further investigated by targeted genotyping. For known POAG-related gene variants in other ethnicities, representative POAG-related single nucleotide polymorphisms (SNPs) from six loci (CDKN2B-AS1, SIX1/SIX6, ATOH7, CDC7-TGFBR3, CAV1, TMCO1) were selected and genotyped from discovery (POAG = 309, heathy = 5,400) and replication cohorts from Korea (POAG = 310, healthy = 5,612 and POAG = 221, healthy = 6,244, respectively). SNP rs116121322 in LRRC27 showed nominally significant association with POAG in the discovery cohort (OR = 29.85, P = 2.2E–06). This SNP was validated in the Korean replication cohort but only in the NTG subgroups (OR = 9.86, P = 0.007). Japanese replication cohort did not show significant association with POAG (P = 0.44). However, the meta-analysis in the entire cohort revealed significant association of rs116121322 with POAG (ORcombined = 10.28, Pcombined = 1.4E–07). The LRRC27 protein expression was confirmed from human trabecular meshwork cells. For gene-based testing, METTL20 showed a significant association in POAG (Pcombined = 0.002) and in the subgroup of NTG (Pcombined = 0.02), whereas ZNF677 were significantly associated with only in the subgroup of high-tension glaucoma (Pcombined = 1.5E–06). In terms of previously known POAG-related variants, rs1900004 in ATOH7 (OR = 1.29, P = 0.0024); rs1063192 (OR = 0.69, P = 0.0006), rs2157719 (OR = 0.63, P = 0.0007), and rs7865618 (OR = 0.63, P = 0.0006) in CDKN2B-AS1, and rs10483727 in SIX1/SIX6 (OR = 0.68, P = 7.9E–05) were nominally associated with the risk of POAG. The replication cohorts revealed significant associations with rs2157719 (OR = 0.72, P = 0.0135), rs1063192 (OR = 0.63, P = 0.0007) and rs7865618 (OR = 0.52, P = 0.0004) in CDKN2B-AS1. A mega-analysis from the entire Korean population revealed significance with rs1063192 (OR = 0.77, P = 6.0E–05), rs2157719 (OR = 0.63, P = 0.0007) and rs7865618 (OR = 0.58, P = 1.9E–06) in CDKN2B-AS1 and with rs10483727 in SIX1/SIX6 (OR = 0.79, P = 9.4E–05), with the same direction of effect between the discovery association and the replication sample. Our findings may provide further genetic backgrounds into the pathogenesis of POAG, especially for the patients who have lower baseline intraocular pressures.원발 개방각녹내장은 정상 안압 범위에서도 발생할 수 있으며, 소위 정상안압녹내장으로 불리는 이러한 유형의 녹내장은 한국과 일본을 비롯한 동아시아국가에서 유병율이 매우 높다. 본 연구에서는 원발 개방각녹내장과 연관되어 있는 저빈도/희귀 유전 변이를 탐색하기 위해 일차 코호트 (원발 개방각녹내장 309명, 정상군 5400명, 모두 한국인)에서 엑솜칩 분석을 수행하였다. 검증을 위해 한국인 코호트 (원발 개방각녹내장 310명, 정상군 5612명)와 일본인 코호트 (원발 개방각녹내장 565명, 정상군 1104명)에서 유전자형을 추가 분석하였다. 타인종에서 원발 개방각녹내장과 연관된 것으로 보고된 대표적인 6개 유전자 (CDKN2B-AS1, SIX1/SIX6, ATOH7, CDC7-TGFBR3, CAV1, TMCO1) 단일염기다형성 (SNP)을 선정하여 일차코호트 (원발 개방각녹내장 309명, 정상군 5400명, 모두 한국인)와 검증코호트 (#1: 원발 개방각녹내장 310명, 정상군 5612명, #2: 원발 개방각녹내장 221명, 정상군 6244명, 모두 한국인)에서 유전자형을 분석하였다. 일차 코호트에서 LRRC27 유전자의 SNP rs116121322이 유의하게 원발 개방각녹내장과 연관되었다 (OR = 29.85, P = 2.2E–06). 이 SNP는 한국인 코호트에서는 추가 검증 되었으나 (OR = 9.86, P = 0.007) 일본인 코호트에서는 원발 개방각녹내장과 유의한 연관이 없었다 (P = 0.44). 그러나 전체 코호트에서 수행한 메타분석에서는 원발 개방각녹내장과 유의한 연관을 보였다 (ORcombined = 10.28, Pcombined = 1.4E–07). 또한 LRRC27 단백질이 인간섬유주세포에서 발현되는 것을 확인하였다. 유전자 수준의 분석에서, METTL20 유전자가 원발 개방각녹내장 (Pcombined = 0.002) 과 정상안압녹내장군 (Pcombined = 0.02)에서 유의한 연관을 보였고 ZNF677 유전자는 고안압녹내장군에서 유의한 연관을 보였다 (Pcombined = 1.5E–06). 타인종에서 원발 개방각녹내장과 연관된 것으로 보고된 유전자 변이 중에서 ATOH7 유전자의 rs1900004 (OR = 1.29, P = 0.0024), CDKN2B-AS1 유전자의 rs1063192 (OR = 0.69, P = 0.0006), rs2157719 (OR = 0.63, P = 0.0007), rs7865618 (OR = 0.63, P = 0.0006) 그리고 SIX1/SIX6 유전자의 rs10483727 (OR = 0.68, P = 7.9E–05)가 원발 개방각녹내장과 유의한 연관을 보였다. 검증코호트에서는 CDKN2B-AS1 유전자의 rs2157719 (OR = 0.72, P = 0.0135), rs1063192 (OR = 0.63, P = 0.0007), rs7865618 (OR = 0.52, P = 0.0004)가 유의한 연관을 보였다. 전체 한국인 코호트 통합 분석에서 CDKN2B-AS1 유전자의 rs1063192 (OR = 0.77, P = 6.0E–05), rs2157719 (OR = 0.63, P = 0.0007), rs7865618 (OR = 0.58, P = 1.9E–06), 그리고 SIX1/SIX6 유전자의 rs10483727 (OR = 0.79, P = 9.4E–05)가 유의하게 원발 개방각녹내장과 연관되었다. 본 연구를 통해 원발 개방각녹내장, 특히 기저안압이 정상범위인 환자들의 유전적 병인에 대한 이해를 높일 것으로 판단된다.Introduction 1 Materials and Methods 4 Results 13 Discussion 39 References 51 Abstract in Korean 60Docto

The investigation of acute optic neuritis: a review and proposed protocol

Optic neuritis is an inflammatory optic neuropathy that affects many patients with multiple sclerosis (MS) at some point during their disease course. Differentiation of acute episodes of MS-associated optic neuritis from other autoimmune and inflammatory optic neuropathies is vital for treatment choice and further patient management, but is not always straightforward. Over the past decade, a number of new imaging, laboratory and electrophysiological techniques have entered the clinical arena. To date, however, no consensus guidelines have been devised to specify how and when these techniques can be most rationally applied for the diagnostic work-up of patients with acute optic neuritis. In this article, we review the literature and attempt to formulate a consensus for the investigation of patients with acute optic neuritis, both in standard care and in research with relevance to clinical treatment trials

Neurofly 2008 abstracts : the 12th European Drosophila neurobiology conference 6-10 September 2008 Wuerzburg, Germany

This volume consists of a collection of conference abstracts

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally

Automated triaging of head MRI examinations using convolutional neural networks

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in $\text{T}_2$-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital ($\Delta$AUC $\leq$ 0.02). A simulation study demonstrated that our model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating feasibility for use in a clinical triage environment.Comment: Accepted as an oral presentation at Medical Imaging with Deep Learning (MIDL) 202