Anran Ran

Anran Ran
The Chinese University of Hong Kong | CUHK · Department of Ophthalmology and Visual Sciences

MMed PhD

About

36
Publications
8,176
Reads
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414
Citations
Citations since 2016
36 Research Items
413 Citations
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Introduction
I currently work at the Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong as a scientific officer. My research interests are glaucoma, myopia, ocular imaging, Artificial Neural Network, and Artificial Intelligence. My current project is "artificial intelligence supported system for glaucoma". My previous project is 'Anyang Childhood Eye Study'.
Additional affiliations
September 2020 - present
The Chinese University of Hong Kong
Position
  • PostDoc Position
Description
  • I am working on ocular imaging, artificial intelligence application in glaucoma, Alzheimer's disease, and diabetic retinopathy.
Education
September 2017 - August 2020
The Chinese University of Hong Kong
Field of study
  • Ophthalmology and Visual Sciences
September 2014 - June 2017
Capital Medical University
Field of study
  • Ophthalmology
September 2011 - June 2013
East China Normal University
Field of study
  • Applied Psychology

Publications

Publications (36)
Article
Background: Spectral-domain optical coherence tomography (SDOCT) can be used to detect glaucomatous optic neuropathy, but human expertise in interpretation of SDOCT is limited. We aimed to develop and validate a three-dimensional (3D) deep-learning system using SDOCT volumes to detect glaucomatous optic neuropathy. Methods: We retrospectively co...
Article
Full-text available
Spectral-domain optical coherence tomography (SDOCT) is a noncontact and noninvasive imaging technology offering three-dimensional (3-D), objective, and quantitative assessment of optic nerve head (ONH) in human eyes in vivo. The image quality of SDOCT scans is crucial for an accurate and reliable interpretation of ONH structure and for further det...
Article
Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, applying DL for glaucoma assessment with optical coherence tomography (OCT), including OCT traditional reports, two-dimens...
Article
Full-text available
Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the field of ocular imaging over the last few years. Specifically, DL has been utilised to detect and classify various ocular diseases on retinal photographs, optical coherence tomography (OCT) images, and OCT-angiography images. In order to achieve good robustne...
Article
Importance Myopia in school-aged children is a public health issue worldwide; consequently, effective interventions to prevent onset and progression are required. Objective To investigate whether SMS text messages to parents increase light exposure and time outdoors in school-aged children and provide effective myopia control. Design, Setting, an...
Article
Purpose of review: Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent ad...
Article
Full-text available
Background There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex—typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs...
Article
Full-text available
PurposeWe aim to develop a multi-task three-dimensional (3D) deep learning (DL) model to detect glaucomatous optic neuropathy (GON) and myopic features (MF) simultaneously from spectral-domain optical coherence tomography (SDOCT) volumetric scans.Methods Each volumetric scan was labelled as GON according to the criteria of retinal nerve fibre layer...
Article
Full-text available
Purpose: To develop a three-dimensional (3D) deep learning algorithm to detect glaucoma using spectral-domain optical coherence tomography (SD-OCT) optic nerve head (ONH) cube scans and validate its performance on ethnically diverse real-world datasets and on cropped ONH scans. Methods: In total, 2461 Cirrus SD-OCT ONH scans of 1012 eyes were ob...
Article
Aims: We investigated the demographic, ocular, diabetes-related and systemic factors associated with a binary outcome of diabetic macular ischaemia (DMI) as assessed by optical coherence tomography angiography (OCTA) evaluation of non-perfusion at the level of the superficial capillary plexus (SCP) and deep capillary plexus (DCP) in a cohort of pa...
Article
Full-text available
Purpose: Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algor...
Article
Full-text available
Purpose: To assess neural changes in perceptual effects induced by myopic defocus and hyperopic defocus stimuli in ametropic and emmetropic subjects using functional magnetic resonance imaging (fMRI). Methods: This study included 41 subjects with a mean age of 26.0 ± 2.9 years. The mean spherical equivalence refraction was −0.54 ± 0.51D in the emme...
Article
Purpose: We aimed to develop and test a deep-learning (DL) system to perform image quality and diabetic macular ischemia (DMI) assessment on OCTA images. Methods: This study included 7,194 OCTA images with diabetes mellitus for training and primary validation, and 960 images from three independent datasets for external testing. A trinary classif...
Article
Deep learning (DL) is a subset of artificial intelligence based on deep neural networks. It has made remarkable breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, there are rising interests in applying DL methods to analyze optical coherence tomography (OCT) and optical coherence tomog...
Article
Objective: Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. Research design and methods: We trained an...
Article
Full-text available
Purpose To develop a deep-learning (DL) system that can detect referable and vision-threatening diabetic retinopathy (RDR and VTDR) from images obtained on ultra-wide field scanning laser ophthalmoscope (UWF-SLO). Design Observational, cross-sectional study. Subjects A total of 9392 UWF-SLO images of 1903 eyes from 1022 subjects with diabetes fro...
Conference Paper
Purpose : To develop a deep learning model that can effectively detect referable and vision-threatening diabetic retinopathy (RDR and VTDR) on images obtained from ultra-wide field scanning laser ophthalmoscope (UWF-SLO). Methods : UWF-SLO (Daytona, Optos, Dunfermline, UK) images were retrospectively collected from subjects with diabetes in CUHK E...
Article
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Article
Full-text available
Deep learning has achieved remarkable success in the optical coherence tomography (OCT) image classification task with substantial labelled B-scan images available. However, obtaining such fine-grained expert annotations is usually quite difficult and expensive. How to leverage the volume-level labels to develop a robust classifier is very appealin...
Article
Full-text available
Purpose: The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance. Methods: There were 2805 Cirrus optical coh...
Preprint
Full-text available
We propose developing and validating a three-dimensional (3D) deep learning system using the entire unprocessed OCT optic nerve volumes to distinguish true glaucoma from normals in order to discover any additional imaging biomarkers within the cube through saliency mapping. The algorithm has been validated against 4 additional distinct datasets fro...
Chapter
Full-text available
Optical Coherence Tomography (OCT) imaging plays an important role in glaucoma diagnosis in clinical practice. Early detection and timely treatment can prevent glaucoma patients from permanent vision loss. However, only a dearth of automated methods has been developed based on OCT images for glaucoma study. In this paper, we present a novel framewo...
Preprint
Full-text available
Optical Coherence Tomography (OCT) imaging plays an important role in glaucoma diagnosis in clinical practice. Early detection and timely treatment can prevent glaucoma patients from permanent vision loss. However, only a dearth of automated methods has been developed based on OCT images for glaucoma study. In this paper, we present a novel framewo...
Article
Full-text available
Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable...
Article
Full-text available
Objective: To investigate the present situation of diagnosis and treatment for primary angle-closure glaucoma (PACG) and primary open-angle glaucoma (POAG) and awareness of the relevant progress among Chinese ophthalmologists. Methods: This study was a cross-sectional, non-randomized sampling survey. Participants were ophthalmologists who attended...
Article
Objective: To understand the hot spots and breakthrough on ophthalmology by searching and reading articles published in The Journal of American Medical Association(JAMA) from the year 2000 to 2014. Design: Literature search and analysis. Participants: Articles on ophthalmology were published from Jan. 2000 to Feb. 2014 in JAMA. Methods: In Pubmed d...
Article
Full-text available
Chinese eye exercises have been implemented in China as an intervention for controlling children’s myopia for over 50 years. This nested case-control study investigated Chinese eye exercises and their association with myopia development in junior middle school children. Outcome measures were the onset and progression of myopia over a two-year perio...
Article
Recently, the distribution characteristics of retinal nerve fiber layer thickness in myopic population have raised scholars' attention. The retinal nerve fiber layer thickness is varied with different refractive statuses, and is correlated to many factors like age, eye elongation, and fundus changes. Further exploration of the relationship between...
Article
Background: To report the thickness of the peripapillary retinal nerve fiber layer (pRNFL) in Chinese children and examine it's association with refractive error, axial length and optic disc parameters. Design: Population based, cross-sectional study. Participants: A total of 2893 seven-year-old children from 11 randomly selected primary schoo...

Questions

Questions (9)
Question
Currently, due to the data privacy and security concerns, there are less institutes willing to share their data. What can be done by both medical and computer science domain to deal with the issue?
Question
There is a trend on applying federated learning in healthcare domain. What the potentials and challenges in your opinion?
Question
AI showed its promise in many fields, but most of researches in AI application on medicine is still "proof-of concept". So how do clinicians feel about AI? What are clincians' concerns about it?
Question
Currently, there are many publications showing the potential AI application in medical imaging. However, most of the studies are "proof-of-concept" and far from real deployment in clinics. One of the major concerns is the "black box" issue. Thus, developments in explainable AI are highly in need.
Question
I always meet the error "Error: 'curves' must contain multiple datasets." when computing CIs for area under the precision-recall curve using the package "Precrec" in R. It seems, I have to use "auc_ci" function on multi datasets but I only have one dataset because there is no cross validation. Is there any way to solve the problem?
Question
I searched and found some suggestions such as "calcAUPRC", "Precrec" or "PRROC" packages, but couldn't find the exact functions for CI calculation. Is there any available function to deal with it?

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Projects

Projects (2)
Project
Using deep learning methods on various medical images for possible clinical assistance.