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Low-cost smartphone-compatible portable ophthalmoscopes can capture visuals of the patient's retina to screen several ophthalmological diseases like glaucoma. The images captured have lower quality and resolution than standard retinography devices but enough for glaucoma screening. Small videos are captured to improve the chance of inspecting the eye properly; however, those videos may not always have enough quality for screening glaucoma, and the patient needs to repeat the inspection later. In this paper, a method for automatic assessment of the quality of videos captured using the D-Eye lens is proposed and evaluated with a personal dataset with 539 videos. Based on two methods developed for retina localization on the images/frames, the Circle Hough Transform method with a precision of 78,12% and the YOLOv7 method with a precision of 99,78%, the quality assessment method automatically decides on the quality of the video by measuring the number of frames of good-quality in each video, according to the chosen threshold. [ABSTRACT FROM AUTHOR]

Abstract Background Diabetic retinopathy (DR), the most common cause of vision loss, is caused by damage to the small blood vessels in the retina. If untreated, it may result in varying degrees of vision loss and even blindness. Since DR is a silent disease that may cause no symptoms or only mild vision problems, annual eye exams are crucial for early detection to improve chances of effective treatment where fundus cameras are used to capture retinal image. However, fundus cameras are too big and heavy to be transported easily and too costly to be purchased by every health clinic, so fundus cameras are an inconvenient tool for widespread screening. Recent technological developments have enabled to use of smartphones in designing small-sized, low-power, and affordable retinal imaging systems to perform DR screening and automated DR detection using image processing methods. In this paper, we investigate the smartphone-based portable retinal imaging systems available on the market and compare their image quality and the automatic DR detection accuracy using a deep learning framework. Results Based on the results, iNview retinal imaging system has the largest field of view and better image quality compared with iExaminer, D-Eye, and Peek Retina systems. The overall classification accuracy of smartphone-based systems are sorted as 61%, 62%, 69%, and 75% for iExaminer, D-Eye, Peek Retina, and iNview images, respectively. We observed that the network DR detection performance decreases as the field of view of the smartphone-based retinal systems get smaller where iNview is the largest and iExaminer is the smallest. Conclusions The smartphone-based retina imaging systems can be used as an alternative to the direct ophthalmoscope. However, the field of view of the smartphone-based retina imaging systems plays an important role in determining the automatic DR detection accuracy.

Objective The aim of the study is to compare performance and ease-of-use (EOU) of optic disk assessment using a smartphone direct ophthalmoscope attachment (D-EYE) to the gold standard direct ophthalmoscope (DO). Design The type of study involved is prospective, randomized, crossover, and educational trial. Participants The participants involved were first year medical students inexperienced in ophthalmoscopy. Methods Optic disks of standardized and volunteer patients were examined using the D-EYE and a conventional DO. Optic disk identification, EOU ratings of the devices, self-reported confidence level in their examination with the devices, and estimation of vertical cup-to-disk ratio (VCDR) were compared. Analyses included Chi-square tests, independent samples t-tests, correlations, and multivariable linear regression. Results Forty-four medical students voluntarily participated in the study. Students using the DO required more attempts (3.57 vs. 2.69, p = 0.010) and time (197.00 vs. 168.02 seconds, p = 0.043) to match the patient's fundus to the correct photograph. Overall EOU between the devices (6.40 vs. 4.79, p

Background: Diabetic retinopathy (DR), the most common cause of vision loss, is caused by damage to the small blood vessels in the retina. If untreated, it may result in varying degrees of vision loss and even blindness. Since DR is a silent disease that may cause no symptoms or only mild vision problems, annual eye exams are crucial for early detection to improve chances of effective treatment where fundus cameras are used to capture retinal image. However, fundus cameras are too big and heavy to be transported easily and too costly to be purchased by every health clinic, so fundus cameras are an inconvenient tool for widespread screening. Recent technological developments have enabled to use of smartphones in designing small-sized, low-power, and affordable retinal imaging systems to perform DR screening and automated DR detection using image processing methods. In this paper, we investigate the smartphone-based portable retinal imaging systems available on the market and compare their image quality and the automatic DR detection accuracy using a deep learning framework. Results: Based on the results, iNview retinal imaging system has the largest field of view and better image quality compared with iExaminer, D-Eye, and Peek Retina systems. The overall classification accuracy of smartphone-based systems are sorted as 61%, 62%, 69%, and 75% for iExaminer, D-Eye, Peek Retina, and iNview images, respectively. We observed that the network DR detection performance decreases as the field of view of the smartphone-based retinal systems get smaller where iNview is the largest and iExaminer is the smallest. Conclusions: The smartphone-based retina imaging systems can be used as an alternative to the direct ophthalmoscope. However, the field of view of the smartphone-based retina imaging systems plays an important role in determining the automatic DR detection accuracy. [ABSTRACT FROM AUTHOR]

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Objective To assess the use of an optical device (D- EYE; Si14 S.p.A.) attached to a modern smartphone ( iPhone 5; Apple Inc.) for imaging the fundus in small animals. Animals studied Five dogs, five cats, and five rabbits with clear media were imaged using a prototype of the D- EYE. Procedure The optical device was composed of lenses, polarizing filters, a beam splitter, a diaphragm, and mirrors, attached to a smartphone via a metal shell. Images were obtained 20 min after pupil dilation with topical 0.5% tropicamide in a darkened room, to ensure maximum pupillary dilation. Focus was set to the infinite when the autofocus was overwhelmed. Light intensity was adapted to each animal via the application (minimum light intensity for imaging the tapetal region, maximum light intensity for imaging the nontapetal region). Both still images and video sequences were recorded for each animal. Results Posterior segment structures were visible in all animals: optic nerve head, tapetum lucidum (when present), nontapetal region, retinal vessels, and choroidal vessels (when the retinal pigment epithelium and the choroidal pigmentation were discreet). Focal light artifacts were common when photographing the tapetum lucidum. Recording videos allowed the visualization of dynamic phenomena. Conclusions The D- EYE assessed appears to be an easy means of obtaining images of the posterior segment structures. [ABSTRACT FROM AUTHOR]

Objective: Th aim of this study is to explore the diagnostic accuracy of smartphone-based D-EYE ophthalmoscopy in the diagnosis of optic nerve head (ONH) abnormalities and screening for glaucoma., Methods: This is a prospective clinical-based validation study performed on 90 patients recruited from a tertiary teaching hospital. Patients underwent dilated fundus examination by slit-lamp and smartphone-based D-EYE fundoscopy operated by two experienced ophthalmologists., Results: The diagnostic accuracy of smartphone-based D-EYE ophthalmoscopy of normal vertical cup-to-disc ratio (VCDR) was acceptable (sensitivity 85.8%-96.4%; specificity 51.4%-96.4%). The D-EYE would often underestimate VCDR values as mean VCDR was significantly lower among overall and glaucoma cohorts (all p-value <0.001) for D-EYE in comparison with slit-lamp. In terms of ONH abnormalities, the D-EYE adequately demonstrated high sensitivity in the identification of only margins-related abnormalities (sensitivity 92.5%-96.6%). Overall, the diagnostic accuracy and agreement between expert ophthalmologists using the D-EYE and the reference slit-lamp examination were significantly poor., Conclusion: We demonstrated that the D-EYE is a highly specific tool for VCDR within the normal range but not in patients with glaucoma. Despite being an easy and portable tool to measure VCDR, it is limited in terms of diagnosing ONH and blood vessel abnormalities.

Objective  The aim of the study is to compare performance and ease-of-use (EOU) of optic disk assessment using a smartphone direct ophthalmoscope attachment (D-EYE) to the gold standard direct ophthalmoscope (DO). Design  The type of study involved is prospective, randomized, crossover, and educational trial. Participants  The participants involved were first year medical students inexperienced in ophthalmoscopy. Methods  Optic disks of standardized and volunteer patients were examined using the D-EYE and a conventional DO. Optic disk identification, EOU ratings of the devices, self-reported confidence level in their examination with the devices, and estimation of vertical cup-to-disk ratio (VCDR) were compared. Analyses included Chi-square tests, independent samples t -tests, correlations, and multivariable linear regression. Results  Forty-four medical students voluntarily participated in the study. Students using the DO required more attempts (3.57 vs. 2.69, p  = 0.010) and time (197.00 vs. 168.02 seconds, p  = 0.043) to match the patient's fundus to the correct photograph. Overall EOU between the devices (6.40 vs. 4.79, p  < 0.001) and overall confidence in examination (5.65 vs. 4.49, p  = 0.003) were greater when using the D-EYE. There were no statistically significant differences in accuracy of VCDR estimations between the two ophthalmoscopes. Conclusion  Smartphone ophthalmoscopy could offer additional learning opportunities in medical education and may be considered in clinical practice by non-specialist physicians given its greater EOU and increased success in visualizing the optic disk., Competing Interests: Conflict of Interest R.C. reports grants from Physicians' Services Incorporated Grant, during the conduct of the study; D.L. reports grants from Physician's Services Incorporated Grant, during the conduct of the study; I.I. reports grants from Physicians' Services Incorporated Grant, during the conduct of the study; .J.K. reports grants from Physician's Services Incorporated Grant, during the conduct of the study; S.B. reports grants from Physician Services Incorporated, during the conduct of the study; W.H. reports grants from Physician's Services Incorporated Grant, during the conduct of the study; M.X. reports grants from Physicians' Services Incorporated Grant, during the conduct of the study., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ).)