Effect of recombinant human nerve growth factor eye drops in patients with dry eye: a phase IIa, open label, multiple-dose study

Background: Dry eye disease (DED) affects more than 14% of the elderly population causing decrease of quality of life, high costs and vision impairment. Current treatments for DED aim at lubricating and controlling inflammation of the ocular surface. Development of novel therapies targeting different pathogenic mechanisms is sought-after. The aim of this study is to evaluate safety and efficacy of recombinant human nerve growth factor (rhNGF) eye drops in patients with DED. Methods: Forty consecutive patients with moderate to severe DED were included in a phase IIa, prospective, open label, multiple-dose, clinical trial to receive rhNGF eye drops at 20 μg/mL (Group 1: G1) or at 4 μg/mL (Group 2: G2) concentrations, two times a day in both eyes for 28 days (NCT02101281). The primary outcomes measures were treatment-emerged adverse events (AE), Symptoms Assessment in Dry Eye (SANDE) scale, ocular surface staining and Schirmer test. Results: Of 40 included patients, 39 completed the trial. Both tested rhNGF eye drop concentrations were safe and well tolerated. Twenty-nine patients experienced at least one AE (14 in G1 and 15 in G2), of which 11 had at least 1 related AE (8 in G1 and 3 in G2). Both frequency and severity of DED symptoms and ocular surface damage showed significant improvement in both groups, while tear function improved only in G1. Conclusions: The data of this study indicate that rhNGF eye drops in both doses is safe and effective in improving symptoms and signs of DED. Randomised clinical trials are ongoing to confirm the therapeutic benefit of rhNGF in DED. Trial registration number: NCT02101281

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

Generative adversarial networks in ophthalmology: what are these and how can they be used?

PURPOSE OF REVIEW: The development of deep learning (DL) systems requires a large amount of data, which may be limited by costs, protection of patient information and low prevalence of some conditions. Recent developments in artificial intelligence techniques have provided an innovative alternative to this challenge via the synthesis of biomedical images within a DL framework known as generative adversarial networks (GANs). This paper aims to introduce how GANs can be deployed for image synthesis in ophthalmology and to discuss the potential applications of GANs-produced images. RECENT FINDINGS: Image synthesis is the most relevant function of GANs to the medical field, and it has been widely used for generating 'new' medical images of various modalities. In ophthalmology, GANs have mainly been utilized for augmenting classification and predictive tasks, by synthesizing fundus images and optical coherence tomography images with and without pathologies such as age-related macular degeneration and diabetic retinopathy. Despite their ability to generate high-resolution images, the development of GANs remains data intensive, and there is a lack of consensus on how best to evaluate the outputs produced by GANs. SUMMARY: Although the problem of artificial biomedical data generation is of great interest, image synthesis by GANs represents an innovation with yet unclear relevance for ophthalmology

Normal approximation and large deviations for the Robbins-Monro Process

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47650/1/440_2004_Article_BF00532261.pd

Longitudinal Study of Optic Disk Perfusion and Retinal Structure in Leber's Hereditary Optic Neuropathy

PURPOSE. The purpose of this study was to evaluate optic disk perfusion and neural retinal structure in patients with subacute Leber's hereditary optic neuropathy (LHON) and LHON carriers, as compared with healthy controls. METHODS. This study included 8 patients with LHON in the subacute stage, 10 asymptomatic carriers of a LHON-associated mitochondrial DNA mutation, and 40 controls. All subjects underwent measurement of the retinal nerve fiber layer (RNFL) thickness, the ganglion cell-inner plexiform layer (GCIPL) thickness using optical coherence tomography and optic disk microvascular perfusion (Mean Tissue [MT]) using laser speckle flowgraphy (LSFG). Patients were re-examined after a median interval of 3 months from the baseline visit. RESULTS. LHON carriers had higher values of RNFL thickness, GCIPL thickness, and disk area than controls (P < 0.05), whereas MT was not different between the two groups (P = 0.936). Median MT and RNFL thickness were 32% and 15% higher in the early subacute stage of the disease than in controls (P < 0.001 and P = 0.001). MT declined below the values of controls during the late subacute stage (P = 0.024), whereas RNFL thickness declined later during the dynamic stage (P < 0.001). GCIPL thickness was lower in patients with LHON than in controls independently of the stage of the disease (P < 0.001). CONCLUSIONS. The high blood flow at the optic disk during the early subacute stage may be the consequence of vasodilation due to nitric oxide release as compensation to mitochondrial impairment. Optic disk perfusion as measured by LSFG is a promising biomarker for LHON diagnosis and monitoring as well as an objective outcome measure for assessing response to therapies

Artificial intelligence and deep learning in ophthalmology

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI 'black-box' algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward

An optical coherence photoacoustic microscopy system using a fiber optic sensor

In this work, a novel fiber optic sensor based on Fabry-Pérot interferometry is adopted in an optical coherence photoacoustic microscopy (OC-PAM) system to enable high-resolution in vivo imaging. The complete OC-PAM system is characterized using the fiber optic sensor for photoacoustic measurement. After characterization, the performance of the system is evaluated by imaging zebrafish larvae in vivo. With a lateral resolution of 3.4 μm and an axial resolution of 3.7 μm in air, the optical coherence microscopy subsystem visualizes the anatomy of the zebrafish larvae. The photoacoustic microscopy subsystem reveals the vasculature of the zebrafish larvae with a lateral resolution of 1.9 μm and an axial resolution of 37.3 μm. As the two modalities share the same sample arm, we obtain inherently co-registered morphological and vascular images. This OC-PAM system provides comprehensive information on the anatomy and vasculature of the zebrafish larvae. Featuring compactness, broad detection bandwidth, and wide detection angle, the fiber optic sensor enables a large field of view with a static sensor position. We verified the feasibility of the fiber optic sensor for dual-modality in vivo imaging. The OC-PAM system, as a non-invasive imaging method, demonstrates its superiority in the investigation of zebrafish larvae, an animal model with increasing significance in developmental biology and disease research. This technique can also be applied for functional as well as longitudinal studies in the future