Privacy-preserving continual learning methods for medical image classification: a comparative analysis.

Background: The implementation of deep learning models for medical image classification poses significant challenges, including gradual performance degradation and limited adaptability to new diseases. However, frequent retraining of models is unfeasible and raises concerns about healthcare privacy due to the retention of prior patient data. To address these issues, this study investigated privacy-preserving continual learning methods as an alternative solution.

Artificial intelligence and digital health in global eye health: opportunities and challenges.

Global eye health is defined as the degree to which vision, ocular health, and function are maximised worldwide, thereby optimising overall wellbeing and quality of life. Improving eye health is a global priority as a key to unlocking human potential by reducing the morbidity burden of disease, increasing productivity, and supporting access to education. Although extraordinary progress fuelled by global eye health initiatives has been made over the last decade, there remain substantial challenges impeding further progress.

Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside.

Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation.

Metaverse and Virtual Health Care in Ophthalmology: Opportunities and Challenges.

The outbreak of the coronavirus disease 2019 has further increased the urgent need for digital transformation within the health care settings, with the use of artificial intelligence/deep learning, internet of things, telecommunication network/virtual platform, and blockchain. The recent advent of metaverse, an interconnected online universe, with the synergistic combination of augmented, virtual, and mixed reality described several years ago, presents a new era of immersive and real-time experiences to enhance human-to-human social interaction and connection. In health care and ophthalmology, the creation of virtual environment with three-dimensional (3D) space and avatar, could be particularly useful in patient-fronting platforms (eg, telemedicine platforms), operational uses (eg, meeting organization), digital education (eg, simulated medical and surgical education), diagnostics, and therapeutics.

Computer-aided detection and abnormality score for the outer retinal layer in optical coherence tomography.

Background: To develop computer-aided detection (CADe) of ORL abnormalities in the retinal pigmented epithelium, interdigitation zone and ellipsoid zone via optical coherence tomography (OCT).

Methods: In this retrospective study, healthy participants with normal ORL, and patients with abnormality of ORL including choroidal neovascularisation (CNV) or retinitis pigmentosa (RP) were included. First, an automatic segmentation deep learning (DL) algorithm, CADe, was developed for the three outer retinal layers using 120 handcraft masks of ORL.

New digital models of care in ophthalmology, during and beyond the COVID-19 pandemic.

COVID-19 has led to massive disruptions in societal, economic and healthcare systems globally. While COVID-19 has sparked a surge and expansion of new digital business models in different industries, healthcare has been slower to adapt to digital solutions. The majority of ophthalmology clinical practices are still operating through a traditional model of 'brick-and-mortar' facilities and 'face-to-face' patient-physician interaction.

Digital Technology for AMD Management in the Post-COVID-19 New Normal.

Purpose: The COVID-19 pandemic has put strain on healthcare systems and the availability and allocation of healthcare manpower, resources and infrastructure. With immediate priorities to protect the health and safety of both patients and healthcare service providers, ophthalmologists globally were advised to defer nonurgent cases, while at the same time managing sight-threatening conditions such as neovascular Age-related Macular Degeneration (AMD). The management of AMD patients both from a monitoring and treatment perspective presents a particular challenge for ophthalmologists.

Implementing the new normal in ophthalmology care beyond COVID-19.

The COVID-19 pandemic has altered the clinical landscape immeasurably. The need to physical distance requires rethinking how we deliver ophthalmic care. Within healthcare, we will need to focus our resources on the five T's: Utilising technology, multidisciplinary clinical teams with wide professional talents need to work efficiently to reduce patient contact time.

Detection of features associated with neovascular age-related macular degeneration in ethnically distinct data sets by an optical coherence tomography: trained deep learning algorithm.

Background: The ability of deep learning (DL) algorithms to identify eyes with neovascular age-related macular degeneration (nAMD) from optical coherence tomography (OCT) scans has been previously established. We herewith evaluate the ability of a DL model, showing excellent performance on a Korean data set, to generalse onto an American data set despite ethnic differences. In addition, expert graders were surveyed to verify if the DL model was appropriately identifying lesions indicative of nAMD on the OCT scans.

Anterior segment optical coherence tomography angiography for iris vasculature in pigmented eyes.

Purpose: To compare anterior segment optical coherence tomography angiography (AS-OCTA) systems in delineating normal iris vessels and iris neovascularisation (NVI) in eyes with pigmented irides.

Methods: Prospective study from January 2019 to June 2019 of 10 consecutive patients with normal pigmented iris, had AS-OCTA scans with a described illumination technique, before using the same protocol in five eyes with NVI (clinical stages 1-3). All scans were sequentially performed using a spectral-domain OCTA (SD-OCTA), and a swept-source OCTA (SS-OCTA, Plex Elite 9000).

Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists.

Objective: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance.

Methods: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information.

Comparison of macular structural and vascular changes in neuromyelitis optica spectrum disorder and primary open angle glaucoma: a cross-sectional study.

Aims: To compare macular structure and vasculature between neuromyelitis optica spectrum disorder (NMOSD) and primary open angle glaucoma (POAG) using optical coherence tomography angiography.

Methods: NMOSD patients (n=124) with/without a history of optic neuritis (ON) (NMO+ON: 113 eyes; NMO-ON: 95 eyes), glaucomatous patients (n=102) with early/advanced glaucoma (G-E: 74 eyes; G-A: 50 eyes) and healthy controls (n=62; 90 eyes) were imaged. The main outcome measures were macular ganglion cell-inner plexiform layer (GC-IPL) thickness, vessel density (VD) and perfusion density (PD) in the superficial capillary plexus, and diagnostic capabilities of the parameters as calculated by area under the curve (AUC).

Prediction of visual outcomes by an artificial neural network following intravitreal injection and laser therapy for retinopathy of prematurity.

Aims: To construct a program to predict the visual acuity (VA), best corrected VA (BCVA) and spherical equivalent (SE) of patients with retinopathy of prematurity (ROP) from 3 to 12 years old after intravitreal injection (IVI) of anti-vascular endothelial growth factor and/or laser photocoagulation treatment.

Methods: This retrospective study employed a feedforward artificial neural network with an error backpropagation learning algorithm to predict visual outcomes based on patient birth data, treatment received and age at follow-up. Patients were divided into two groups based on prior treatments.

Retinal vascular geometry and 6 year incidence and progression of diabetic retinopathy.

Aims/hypothesis: We aimed to examine prospectively the association between a range of retinal vascular geometric variables measured from retinal photographs and the 6 year incidence and progression of diabetic retinopathy.

Methods: We conducted a prospective, population-based cohort study of Asian Malay individuals aged 40-80 years at baseline (n = 3280) who returned for a 6 year follow-up. Retinal vascular geometric variables (tortuosity, branching, fractal dimension, calibre) were measured from baseline retinal photographs using a computer-assisted program (Singapore I Vessel Assessment).

Biomarkers of Diabetic Retinopathy.

Diabetic retinopathy (DR), a leading cause of acquired vision loss, is a microvascular complication of diabetes. While traditional risk factors for diabetic retinopathy including longer duration of diabetes, poor blood glucose control, and dyslipidemia are helpful in stratifying patient's risk for developing retinopathy, many patients without these traditional risk factors develop DR; furthermore, there are persons with long diabetes duration who do not develop DR. Thus, identifying biomarkers to predict DR or to determine therapeutic response is important.

Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore.

Purpose: To determine the incremental cost-effectiveness of a new telemedicine technician-based assessment relative to an existing model of family physician (FP)-based assessment of diabetic retinopathy (DR) in Singapore from the health system and societal perspectives.

Design: Model-based, cost-effectiveness analysis of the Singapore Integrated Diabetic Retinopathy Program (SiDRP).

Participants: A hypothetical cohort of patients aged 55 years with type 2 diabetes previously not screened for DR.

Choroidal Structural Changes in Myopic Choroidal Neovascularization After Treatment With Antivascular Endothelial Growth Factor Over 1 Year.

Purpose: To evaluate choroidal structural changes in eyes with myopic choroidal neovascularization (mCNV) treated with anti-VEGF over 12 months.

Methods: We prospectively evaluated subfoveal choroidal thickness (SFCT) and choroidal vascularity index (CVI) using spectral-domain optical coherence tomography (SD-OCT) at baseline, 6, and 12 months in both eyes in patients presenting with unilateral mCNV. Choroidal vascularity index was defined as the ratio of luminal area to total choroidal area after SD-OCT images were binarized digitally.

Ophthalmology simulation for undergraduate and postgraduate clinical education.

This is a review education paper on the current ophthalmology simulators utilized worldwide for undergraduate and postgraduate training. At present, various simulators such as the EYE Exam Simulator (Kyoto Kagaku Co. Ltd.