ORCID Profile
0000-0001-8954-1433
Current Organisations
ODOCS EYE CARE
,
Dunedin Public Hospital
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Publisher: Bentham Science Publishers Ltd.
Date: 10-11-2021
DOI: 10.2174/1874364102115010243
Abstract: We aim to discuss an inexpensive method of conducting binocular indirect ophthalmoscopy without the binocular indirect ophthalmoscope headset. This technique only requires the examiner to hold a light source between their eyes and hold a condensing lens at an arm’s length. This may be particularly valuable in resource-poor settings.
Publisher: Springer Science and Business Media LLC
Date: 18-08-2023
Publisher: Informa UK Limited
Date: 20-04-2015
DOI: 10.3109/02688697.2015.1029432
Abstract: A 61-year-old male presented with primary intraosseous osteolytic meningioma and transcalvaria herniation. This is an extremely rare condition with only 16 case reports of osteolytic intraosseous meningioma. This case is unique because it was accompanied by a transcalvaria herniation.
Publisher: Springer Science and Business Media LLC
Date: 22-04-2015
Publisher: Wiley
Date: 15-03-2023
DOI: 10.1111/CEO.14220
Abstract: With the promise of gene replacement therapy, eligible males and females with X‐linked inherited retinal dystrophy (XL‐IRD) should be identified. Retrospective observational cohort study to establish the phenotypic and genotypic spectrum of XL‐IRD within New Zealand (NZ). Thirty‐two probands, including 9 females, with molecularly proven XL‐IRD due to RP2 or RPGR mutations, and 72 family members, of which 43 were affected, were identified from the NZ IRD Database. Comprehensive ophthalmic phenotyping, familial cosegregation, genotyping, and bioinformatics were undertaken. Main outcome measures were: RP2 and RPGR pathogenic variant spectrum, phenotype in males and females (symptoms, age of onset, visual acuity, refraction, electrophysiology, autofluorescence, retinal appearance), and genotype–phenotype correlation. For 32 families, 26 unique pathogenic variants were identified in RP2 ( n = 6, 21.9% of all families), RPGR exons 1–14 ( n = 10, 43.75%), and RPGR‐ ORF15 ( n = 10, 34.3%). Three RP2 and 8 RPGR exons 1–14 variants are novel, rare, and cosegregate. Thirty‐one percent of carrier females were significantly affected, with 18.5% of families initially classified as autosomal dominant. Of five Polynesian families, 80% had novel disease‐causing variants. One Māori family showed keratoconus segregating with an ORF15 variant. Significant disease was present in 31% of genetically proven female carriers, often leading to an erroneous presumption of the inheritance pattern. Pathogenic variants in 44% of the families were in exon 1–14 of RPGR , more frequent than usually described, which may inform the gene testing algorithm. Proving cosegregation in families for novel variants and identifying affected females and males translates to optimised clinical care and potential for gene therapy.
Publisher: Informa UK Limited
Date: 10-2021
DOI: 10.2147/OPTH.S294428
Publisher: OMICS Publishing Group
Date: 2015
Publisher: Wiley
Date: 04-10-2017
DOI: 10.1111/CEO.13056
Abstract: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Retrospective audit. Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Area under the receiver operating characteristic curve, sensitivity and specificity. For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema.
Publisher: Informa UK Limited
Date: 07-12-2016
DOI: 10.1080/03091902.2016.1264491
Abstract: With the advancement in mobile technology, smartphone retinal photography is becoming a popular practice. However, there is limited information about the safety of the latest smartphones used for retinal photography. This study aims to determine the photobiological risk of iPhone 6 and iPhone 6 plus when used in conjunction with a 20Diopter condensing lens for retinal photography. iPhone 6 and iPhone 6 plus (Apple, Cupertino, CA) were used in this study. The geometrical setup of the study was similar to the indirect ophthalmoscopy technique. The phone was set up at one end of the bench with its flash turned on at maximal brightness a 20 Dioptre lens was placed 15 cm away from the phone. The light that passes through the lens was measured with a spectroradiometer and an illuminance probe at the other end to determine the spectral profile, spatial irradiance, radiant power emitted by the phone's flash. Trigonometric and lens formula were applied to determine the field of view and retinal surface in order to determine the weighted retinal irradiance and weighted retinal radiant exposure. Taking ocular transmission and the distribution of the beam's spatial irradiance into account, the weighted retinal irradiance is 1.40 mW/cm Our study concluded that the photobiological risk posed by iPhone 6 indirect ophthalmoscopy was at least 1 order of magnitude below the safety limits set by the ISO15004-2.2.
Publisher: Informa UK Limited
Date: 11-2016
DOI: 10.1111/CXO.12381
Abstract: The recent advances in mobile technology have made the smartphone a powerful and accessible tool. This article describe the development of a novel smartphone-based anterior segment microscope that is compatible with tele-manufacturing. The anterior segment microscope is equipped with both cobalt-blue and red-free filters that can be used for clinical photo-documentation. The digital files of the microscope are transferrable and compatible with additive-manufacturing. Therefore, the entire device can be locally manufactured with rapid prototyping techniques such as 3D printing.
Publisher: Informa UK Limited
Date: 02-2020
DOI: 10.2147/OPTH.S238897
Publisher: Informa UK Limited
Date: 06-2022
DOI: 10.2147/OPTH.S362342
Publisher: Springer Science and Business Media LLC
Date: 14-11-2016
Publisher: MDPI AG
Date: 30-08-2023
DOI: 10.3390/DIAGNOSTICS13172810
Abstract: Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model’s performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices.
Publisher: Wiley
Date: 15-11-2018
DOI: 10.1111/CEO.13433
Abstract: Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical features at the point of care. We aimed to develop an AI system that can detect several fundus pathologies and report relevant clinical features. Convolutional neural network training with retrospective data set. Colour fundus photos were obtained from publicly available fundus image databases. Images were uploaded to a web-based AI platform for training and validation of AI classifiers. Separate classifiers were created for each fundus pathology and clinical feature. Accuracy, sensitivity, specificity and area under receiver operating characteristic curve (AUC) for each classifier. We obtained 4435 images from publicly available fundus image databases. AI classifiers were developed for each disease state above. Although statistical performance was limited by the small s le size, average accuracy was 89%, average sensitivity was 75%, average specificity was 89% and average AUC was 0.58. This study is a proof-of-concept AI system that could be implemented within a diabetic photo-screening pathway. Performance was promising but not yet at the level that would be required for clinical application. We have shown that it is possible for clinicians to develop AI classifiers with no previous programming or AI knowledge, using standard laptop computers.
Publisher: Bentham Science Publishers Ltd.
Date: 10-11-2021
DOI: 10.2174/1874364102115010206
Abstract: The advancement of smartphone camera technology allowing a smaller, high-resolution forward-facing camera on a smartphone allows a user to see the image they are about to capture of themselves at arm’s length, therefore taking a ‘selfie’ image of themselves. The idea of a ‘selfie’ in a clinical setting is novel, but the exploration of this as a concept has been made necessary as COVID-19 infection and transmission risk is based on the proximity, that is, a susceptible person coming near to the person, who is infected. This report discusses an innovative smartphone-based device, oDocs nun IR, a retinal imaging device, as a tool for taking selfie retinal images/videos by patients, that could be later analyzed by the specialists/optometrists over the teleophthalmology portal.
Publisher: Elsevier
Date: 2020
Publisher: MDPI AG
Date: 18-05-2023
DOI: 10.3390/JOF9050589
Abstract: The present article reports on the management of six different and rare cases of fungal keratitides, two of which have never been documented in previous literature. This is a case series of six patients with rare fungal keratitides managed at a quaternary eye referral unit, Sydney Eye Hospital, Australia over a period of 7 months (May to December, 2022). The order of occurrence of fungi isolated was Scedosporium apiospermum, Lomenstospora prolificans, Cladosporium spp., Paecilomyces, Syncephalastrum racemosum and Quambalaria spp. A combination of medical and surgical interventions was employed, including topical and systemic anti-fungal therapy, with one requiring therapeutic penetrating keratoplasty and another eventuating in evisceration. Two patients were successfully treated with corneal debridement and two others required pars plana vitrectomy with anterior chamber washout. It is important to remain vigilant with monitoring patient symptoms and correlating with clinical signs to guide antifungal therapy even in the context of confirmed culture and sensitivity results.
Publisher: Informa UK Limited
Date: 03-04-2022
DOI: 10.1080/17434440.2022.2070004
Abstract: The present study proposes a new hand-held non-mydriatic fundus camera for retinal imaging. The goal is to design a fundus camera which is equally effective in both clinical and telemedicine scenarios. A new retinal illumination approach is proposed to address the main dilemma of the optical design, i.e. balancing efficacy with structural simplicity. This is achieved by symmetrical and co-axial placement of multiple illumination sources along the optical pathway. Each illumination source includes a white and a Near Infra-Red (NIR) LED, which are placed adjacent to each other. Hence, the camera can produce a view-finder with NIR illumination without the need for additional beam-splitters and filters. The proposed design blends the structural simplicity of the 'off-axis illumination with the wide field of view and uniform illumination of the 'ring' illumination. Moreover, the camera is designed to work with Android-based smartphones, which can easily be mounted and interfaced. The efficacy of the proposed camera is determined by ocular safety analysis and comparative evaluation with a table-top fundus camera. The results convincingly demonstrate the ability of the proposed camera as a primary driver of a wide-scale screening program in both clinical and remote resource constraint environments.
No related grants have been discovered for Sheng Chiong Hong.