Implementation and Evaluation of a Mobile Retinal Image Acquisition System for Screening Diabetic Retinopathy: Study Protocol

Screening diabetic retinopathy, a major cause of blindness, is time-consuming for ophthalmologists and has some constrains in achieving full coverage and attendance. The handheld fundus camera EyeFundusScope was recently developed to expand the scale of screening, drawing on images acquired in primary care and telescreening made by ophthalmologists or a computer-aided diagnosis (CADx) system. This study aims to assess the diagnostic accuracy of the interpretation of images captured using EyeFundusScope and perform its technical evaluation, including image quality, functionality, usability, and acceptance in a real-world clinical setting. Physicians and nurses without training in ophthalmology will use EyeFundusScope to take pictures of the retinas of patients with diabetes and the images will be classified for the presence or absence of diabetic retinopathy and image quality by a panel of ophthalmologists. A subgroup of patients will also be examined with the reference standard tabletop fundus camera. Screening results provided by the CADx system on images taken with EyeFundusScope will be compared against the ophthalmologists’ analysis of images taken with the tabletop fundus camera. Diagnostic accuracy measures with 95% confidence intervals (CIs) will be calculated for positive and negative test results. Proportion of each category of image quality will be presented. Usability and acceptance results will be presented qualitatively.

Developing portable widefield fundus camera for teleophthalmology: Technical challenges and potential solutions

A portable, low cost, widefield fundus camera is essential for developing affordable teleophthalmology. However, conventional trans-pupillary illumination used in traditional fundus cameras limits the field of view (FOV) in a snapshot image, and frequently requires pharmacologically pupillary dilation for reliable examination of eye conditions. This minireview summarizes recent developments in alternative illumination approaches for widefield fundus photography. Miniaturized indirect illumination has been used to enable compact design for developing low cost, portable, widefield fundus camera. Contact mode trans-pars-planar illumination has been validated for ultra-widefield fundus imaging of infant eyes. Contact-free trans-pars-planar illumination has been explored for widefield imaging of adult eyes. Trans-palpebral illumination has been also demonstrated in a smartphone-based widefield fundus imager to foster affordable teleophthalmology.

Technical light-field setup for 3D imaging of the human nerve head validated with an eye model

With the new technology of 3D light field (LF) imaging, fundus photography can be expanded to provide depth information. This increases the diagnostic possibilities and additionally improves image quality by digitally refocusing. To provide depth information in the human optic nerve head such as in glaucoma diagnostics, a mydriatic fundus camera was upgraded with an LF imager. The aim of the study presented here was the validation of the technical setup and resulting depth estimations with an appropriate eye model. The technical setup consisted of a mydriatic fundus camera (FF450, Carl Zeiss Meditec AG, Jena, Germany) and an LF imager (R12, Raytrix GmbH, Kiel, Germany). The field of view was set to 30°. The eye model (24.65 mm total length) consisted of a two-lens optical system and interchangeable fundus models with papilla excavations from 0.2 to 1 mm in steps of 0.2 mm. They were coated with red acrylic lacquer and vessels were drawn with a thin brush. 15 images were taken for each papilla depth illuminated with green light (wavelength 520 nm ± 20 nm). Papilla depth was measured from the papilla ground to the surrounding flat region. All 15 measurements for each papilla depth were averaged and compared to the printed depth. It was possible to perform 3D fundus imaging in an eye model by means of a novel LF-based optical setup. All LF images could be digitally refocused subsequently. Depth estimation in the eye model was successfully performed over a 30° field of view. The measured virtual depth and the printed model papilla depth is linear correlated. The presented LF setup allowed high-quality 3D one-shot imaging and depth estimation of the optic nerve head in an eye model.

Comparison of Retinal Vascular Arcade Trajectory Between Eyes with an Idiopathic Macular Hole and the Healthy Fellow Eye

Purpose: We aimed to present a method to evaluate the retinal arcade vascular trajectory by measuring the distance between the retinal veins in eyes with idiopathic macular hole (IMH) and compare this measurement with healthy fellow eyes.Methods: In this Cross-sectional study18 patients with unilateral IMH were enrolled. We used standard fundus photographs, which were obtained from each eye with a digital fundus camera using a 55-degree lens. The calculation of the retinal arcade vascular trajectories was done by drawing and measuring 5 vertical lines within the macular area. Results: The mean age of the patients was 65±9 years. The between-groups differences of each vertical line were not statistically significant, except for the differences between the most temporal line, which was greater in the diseased group (9388 vs. 8322 mm; P=0.034). The ratio between the 5th (most temporal) and the third (fovea-center) vertical lines was greater than 1 (V-shape) in 72% of eyes with a macular hole, whereas it was less than one (U-shape) in 78% of control eyes (P=0.003). Conclusions: We demonstrated that in eyes with an IMH the vascular arcade has more tendency to diverge on its path temporal to the fovea.

Teaching the Technical Essentials of Direct Ophthalmoscopy Using a Portable Fundus Camera

Aim: A portable fundus camera could allow the third person to observe the technique essentials of direct ophthalmoscopy from a real time screen. This study was designed to compare the proficiency of teaching direct ophthalmoscopy using a portable camera with conventional way in medical students. Methods: Medical students of fourth year were invited to participate the study. At baseline, the participants were taught fundoscopy with a conventional direct ophthalmoscope shortly. Then they were randomized to be taught the skill of fundoscopy either with a portable fundus camera or with a conventional direct ophthalmoscope as control for two days. Accuracy tests to match a subject’s fundus with one of the four photographs after examining an undilated eye using a direct ophthalmoscope were performed at baseline and end point. Accuracy test scores and self-reported confidence were compared between the two groups. Results: A total of 160 students participated the study, with 79 assigned to the intervention group, and 81 to the control group. All the students finished the study. At baseline, there was no difference in accuracy test score between the two groups. After two-day training session, the accuracy score improved in 26/79 (32.9%) students of intervention group versus 15/81 (18.5%) of the control group (p=0.037). At end point, a total of 39/79 (49.4%) students in the intervention groups versus 25/81 (30.9%) in the control group identified the correct fundus photograph (p=0.017). The confidence levels were significantly higher in the intervention group than the control group. Conclusions: Teaching direct ophthalmoscope using a portable fundus camera is associated with improved accuracy score and elevated confidence level in medical students when compared with conventional method.

Detection of Glaucoma using ORB (Oriented FAST and Rotated BRIEF) Feature Extraction

This research was utilized to identify glaucoma, a type of eye illness. This endeavor necessitates the use of pictures from the fundus camera for image processing. This study reflects the effort done to detect glaucoma-affected eyes utilizing image feature extraction using Oriented FAST and Rotated BRIEF (ORB). ORB is a binary descriptor approach that is based on BRIEF and is highly fast. This technique is insensitive to picture noise and is invariant to any rotation. ORB is two orders of magnitude faster than SURF and performs similarly to SIFT. It is more efficient than other texture analysis methods. It is less computationally difficult than other approaches in the literature. This technique extracts features and detects texture by inspecting each pixel of the retina picture. It was trained on 160 fundus pictures of normal and glaucoma-affected retinas. After that, any healthy or glaucoma-affected eye may be easily recognized by obtaining an accurate eye picture. The results reveal that this technique has a precision and accuracy of more than 90%.

Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone

Background/aimsTo validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone.MethodsA training dataset consisting of 1364 colour fundus photographs with glaucomatous indications and 1768 colour fundus photographs without glaucomatous features was obtained using an ordinary fundus camera. The testing dataset consisted of 73 eyes of 73 patients with glaucoma and 89 eyes of 89 normative subjects. In the testing dataset, fundus photographs were acquired using an ordinary fundus camera and a smartphone. A deep learning algorithm was developed to diagnose glaucoma using a training dataset. The trained neural network was evaluated by prediction result of the diagnostic of glaucoma or normal over the test datasets, using images from both an ordinary fundus camera and a smartphone. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC).ResultsThe AROC with a fundus camera was 98.9% and 84.2% with a smartphone. When validated only in eyes with advanced glaucoma (mean deviation value < −12 dB, N=26), the AROC with a fundus camera was 99.3% and 90.0% with a smartphone. There were significant differences between these AROC values using different cameras.ConclusionThe usefulness of a deep learning algorithm to automatically screen for glaucoma from smartphone-based fundus photographs was validated. The algorithm had a considerable high diagnostic ability, particularly in eyes with advanced glaucoma.