Traditional Fundus Camera Research Articles

Service Quality and Residents' Preferences for Facilitated Self-Service Fundus Disease Screening: Cross-Sectional Study.

Fundus photography is the most important examination in eye disease screening. A facilitated self-service eye screening pattern based on the fully automatic fundus camera was developed in 2022 in Shanghai, China; it may help solve the problem of insufficient human resources in primary health care institutions. However, the service quality and residents' preference for this new pattern are unclear. This study aimed to compare the service quality and residents' preferences between facilitated self-service eye screening and traditional manual screening and to explore the relationships between the screening service's quality and residents' preferences. We conducted a cross-sectional study in Shanghai, China. Residents who underwent facilitated self-service fundus disease screening at one of the screening sites were assigned to the exposure group; those who were screened with a traditional fundus camera operated by an optometrist at an adjacent site comprised the control group. The primary outcome was the screening service quality, including effectiveness (image quality and screening efficiency), physiological discomfort, safety, convenience, and trustworthiness. The secondary outcome was the participants' preferences. Differences in service quality and the participants' preferences between the 2 groups were compared using chi-square tests separately. Subgroup analyses for exploring the relationships between the screening service's quality and residents' preference were conducted using generalized logit models. A total of 358 residents enrolled; among them, 176 (49.16%) were included in the exposure group and the remaining 182 (50.84%) in the control group. Residents' basic characteristics were balanced between the 2 groups. There was no significant difference in service quality between the 2 groups (image quality pass rate: P=.79; average screening time: P=.57; no physiological discomfort rate: P=.92; safety rate: P=.78; convenience rate: P=.95; trustworthiness rate: P=.20). However, the proportion of participants who were willing to use the same technology for their next screening was significantly lower in the exposure group than in the control group (P<.001). Subgroup analyses suggest that distrust in the facilitated self-service eye screening might increase the probability of refusal to undergo screening (P=.02). This study confirms that the facilitated self-service fundus disease screening pattern could achieve good service quality. However, it was difficult to reverse residents' preferences for manual screening in a short period, especially when the original manual service was already excellent. Therefore, the digital transformation of health care must be cautious. We suggest that attention be paid to the residents' individual needs. More efficient man-machine collaboration and personalized health management solutions based on large language models are both needed.

IDF23-0374 Agreement between graders for DR screening between smartphone-based retinal camera and traditional fundus camera in LMIC

IDF23-0374 Agreement between graders for DR screening between smartphone-based retinal camera and traditional fundus camera in LMIC

Smart Phone based Fundus Imaging for Diabetic Retinopathy Detection

INTRODUCTION: Diabetic retinopathy (DR) is one of the consequences of diabetes which if untreated may lead to loss of vision. Generally, for DR detection, retinal images are obtained using a traditional fundus camera. A recent trend in the acquisition of eye fundus images is the usage of smartphones to acquire images.&#x0D; OBJECTIVES: This paper focuses on the study of existing works which incorporated smartphones for obtaining fundus images and various devices available in the market. Also, the common datasets used for carrying out DR detection using smartphone-based fundus images as well as the classification models used for the diagnosis of DR are explored.&#x0D; METHODS: A search of information was carried out on articles based on DR detection from fundus images published in the state-of-the-art literatures.&#x0D; RESULTS: Majority of the works uses SBFI devices like 20D lens, EyeExaminer etc. to obtain fundus image. The common databases used for the study are EyePACS, Messidor, etc. and the classification models mostly rely on deep learning frameworks.&#x0D; CONCLUSION: The use of smartphones for capturing fundus images for DR detection are explored. Smartphone devices, datasets used for the study and currently available classification models for SBFI based DR detection are discussed in detail. This paper portrays various approaches currently being employed in SBFI based DR detection.

Widefield and Ultra-Widefield Retinal Imaging: A Geometrical Analysis.

Diabetic retinopathy (DR) often causes a wide range of lesions in the peripheral retina, which can be undetected when using a traditional fundus camera. Widefield (WF) and Ultra-Widefield (UWF) technologies aim to significantly expand the photographable retinal field. We conducted a geometrical analysis to assess the field of view (FOV) of WF and UWF imaging, comparing it to the angular extension of the retina. For this task, we shot WF images using the Zeiss Clarus 500 fundus camera (Carl Zeiss Meditec, Jena, Germany). Approximating the ocular bulb to an ideal sphere, the angular extension of the theoretically photographable retinal surface was 242 degrees. Performing one shot, centered on the macula, it was possible to photograph a retinal surface of ~570 mm2, with a FOV of 133 degrees. Performing four shots with automatic montage, we obtained a retinal surface area of ~1100 mm2 and an FOV of 200 degrees. Finally, performing six shots with semi-automatic montage, we obtained a retinal surface area of ~1400 mm2 and an FOV of 236.27 degrees, which is close to the entire surface of the retina. WF and UWF imaging allow the detailed visualization of the peripheral retina, with significant impact on the diagnosis and management of DR.

Technical and optical aspects of smartphone-based fundus photography : Possibilities and limitations in practice

Smartphone-based adapters for fundus photography offer acost-effective alternative to traditional fundus cameras. The various offers vary widely in design, size, purchasing costs, user-friendliness, and image quality when compared to each other and to traditional fundus cameras. The aim is to enable the reader to understand the technical and optical aspects of fundus photography and therefore to evaluate the various offers of traditional fundus cameras and adaptors for smartphone-based fundus photography with respect to their personal requirements. The technical and optical principles and aspects of fundus photography are explained and the various approaches to illumination, imaging, image capture and alignment are discussed. The association between field of view, pupil size, mydriasis and image quality are discussed as well as the challenges and difficulties of aligning the fundus camera relative to the patient's eye. The most important technical aspects of afundus camera are the avoidance of disturbing reflections when taking photographs, an acceptable field of view angle, the user-friendliness during alignment, success rate of imaging and the question whether mydriasis is necessary. The development of adapters for smartphone-based fundus photography has resulted in interesting offers, which make fundus photography available to awider user spectrum at low purchase costs. The offers on the market vary widely. It is therefore difficult to reach adefinitive evaluation; however, smartphone-based adapters mostly have limitations with respect to user-friendliness, the size of the field of view and image quality.

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.

Ultra-wide field retinal imaging: A wider clinical perspective.

The peripheral retina is affected in a variety of retinal disorders. Traditional fundus cameras capture only a part of the fundus even when montaging techniques are used. Ultra-wide field imaging enables us to delve into the retinal periphery in greater detail. It not only facilitates assessing color images of the fundus, but also fluorescein angiography, indocyanine green angiography, fundus autofluorescence, and red and green free images. In this review, a literature search using the keywords “ultra-widefield imaging”, “widefield imaging”, and “peripheral retinal imaging” in English and non-English languages was done and the relevant articles were included. Ultra-wide field imaging has made new observations in the normal population as well as in eyes with retinal disorders including vascular diseases, degenerative diseases, uveitis, age-related macular degeneration, retinal and choroidal tumors and hereditary retinal dystrophies. This review aims to describe the utility of ultra-wide field imaging in various retinal disorders.

Optical design and fabrication of a smartphone fundus camera.

Fundus examination plays an important part in a medical setting. The fundus camera is one of the detection instruments used in obtaining fundus images, which can reflect information about disease and other conditions. However, traditional fundus cameras have many disadvantages in regard to data sharing, image recognition, and processing, as well as doctor-patient communication. In recent years, mobile medical systems have gradually become more prevalent in medical and health system environments. In this paper, we propose a design method for a smartphone fundus camera consisting of an illumination system and an imaging system. The end of the system can be combined with a smartphone to take the fundus images directly. We manufactured a prototype, designed an artificial eye model, and carried out a series of experiments. The results show that we can get fundus images clearly, and the imaging system will be able to correct refractive errors ranging from -8D∼+8D. The spatial resolution of the system is up to 15 µm. This is a portable device with an overall size of 160mm×160mm×80mm and a weight of 540 g. It has the advantages of lower price, simple operation, high resolution, and compact size, making it suitable as a portable ocular monitoring device.

Current status and future possibilities of retinal imaging in diabetic retinopathy care applicable to low- and medium-income countries.

Diabetic retinopathy (DR) is a leading cause of blindness among adults and the numbers are projected to rise. There have been dramatic advances in the field of retinal imaging since the first fundus image was captured by Jackman and Webster in 1886. The currently available imaging modalities in the management of DR include fundus photography, fluorescein angiography, autofluorescence imaging, optical coherence tomography, optical coherence tomography angiography, and near-infrared reflectance imaging. These images are obtained using traditional fundus cameras, widefield fundus cameras, handheld fundus cameras, or smartphone-based fundus cameras. Fluorescence lifetime ophthalmoscopy, adaptive optics, multispectral and hyperspectral imaging, and multicolor imaging are the evolving technologies which are being researched for their potential applications in DR. Telemedicine has gained popularity in recent years as remote screening of DR has been made possible. Retinal imaging technologies integrated with artificial intelligence/deep-learning algorithms will likely be the way forward in the screening and grading of DR. We provide an overview of the current and upcoming imaging modalities which are relevant to the management of DR.

Automatic Identification of Referral-Warranted Diabetic Retinopathy Using Deep Learning on Mobile Phone Images.

PurposeTo evaluate the performance of a deep learning algorithm in the detection of referral-warranted diabetic retinopathy (RDR) on low-resolution fundus images acquired with a smartphone and indirect ophthalmoscope lens adapter.MethodsAn automated deep learning algorithm trained on 92,364 traditional fundus camera images was tested on a dataset of smartphone fundus images from 103 eyes acquired from two previously published studies. Images were extracted from live video screenshots from fundus examinations using a commercially available lens adapter and exported as a screenshot from live video clips filmed at 1080p resolution. Each image was graded twice by a board-certified ophthalmologist and compared to the output of the algorithm, which classified each image as having RDR (moderate nonproliferative DR or worse) or no RDR.ResultsIn spite of the presence of multiple artifacts (lens glare, lens particulates/smudging, user hands over the objective lens) and low-resolution images achieved by users of various levels of medical training, the algorithm achieved a 0.89 (95% confidence interval [CI] 0.83–0.95) area under the curve with an 89% sensitivity (95% CI 81%–100%) and 83% specificity (95% CI 77%–89%) for detecting RDR on mobile phone acquired fundus photos.ConclusionsThe fully data-driven artificial intelligence-based grading algorithm herein can be used to screen fundus photos taken from mobile devices and identify with high reliability which cases should be referred to an ophthalmologist for further evaluation and treatment.Translational RelevanceThe implementation of this algorithm on a global basis could drastically reduce the rate of vision loss attributed to DR.

Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems

Diabetic Retinopathy (DR) may result in various degrees of vision loss and even blindness if not diagnosed in a timely manner. Therefore, having an annual eye exam helps early detection to prevent vision loss in earlier stages, especially for diabetic patients. Recent technological advances made smartphone-based retinal imaging systems available on the market to perform small-sized, low-powered, and affordable DR screening in diverse environments. However, the accuracy of DR detection depends on the field of view and image quality. Since smartphone-based retinal imaging systems have much more compact designs than a traditional fundus camera, captured images are likely to be the low quality with a smaller field of view. Our motivation in this paper is to develop an automatic DR detection model for smartphone-based retinal images using the deep learning approach with the ResNet50 network. This study first utilized the well-known AlexNet, GoogLeNet, and ResNet50 architectures, using the transfer learning approach. Second, these frameworks were retrained with retina images from several datasets including EyePACS, Messidor, IDRiD, and Messidor-2 to investigate the effect of using images from the single, cross, and multiple datasets. Third, the proposed ResNet50 model is applied to smartphone-based synthetic images to explore the DR detection accuracy of smartphone-based retinal imaging systems. Based on the vision-threatening diabetic retinopathy detection results, the proposed approach achieved a high classification accuracy of 98.6%, with a 98.2% sensitivity and a 99.1% specificity while its AUC was 0.9978 on the independent test dataset. As the main contributions, DR detection accuracy was improved using the transfer learning approach for the ResNet50 network with publicly available datasets and the effect of the field of view in smartphone-based retinal imaging was studied. Although a smaller number of images were used in the training set compared with the existing studies, considerably acceptable high accuracies for validation and testing data were obtained.

Smartphone Assisted Fundus Fundoscopy/Photography

With the introduction of portable fundus photography, the possibility of providing adequate eye care and reducing its cost globally became attainable. Smartphone photography currently plays a vital role in outreach programs where there is a lack of providers and limited availability of traditional fundus cameras. Smartphones are an attractive option in portable ophthalmoscopy due to their widespread availability. Newer techniques have allowed the acquisition of high-quality images that compare to images obtained with the more expensive conventional fundus cameras. Available options include slit lamp attachments, adapters for direct ophthalmoscopy, and the use of smartphones with handheld or mounted lenses as an indirect ophthalmoscope. Telemedicine has a substantial ability to reach underserved populations without compromising the quality of care. The continuous improvement in smartphone technology establishes its integral role in tele-ophthalmology. Further validation studies are needed to prove its advantages and shape its implementation strategies.

Comparative study of teleophthalmology devices: Smartphone adapted ophthalmoscope, robotic ophthalmoscope, and traditional fundus camera - The recent advancements in telemedicine

Comparative study of teleophthalmology devices: Smartphone adapted ophthalmoscope, robotic ophthalmoscope, and traditional fundus camera - The recent advancements in telemedicine

Quality and Diagnostic Utility of Mydriatic Smartphone Photography: The Smartphone Ophthalmoscopy Reliability Trial.

Establish quality and diagnostic utility of mydriatic smartphone ophthalmoscopy (SO) fundus images compared to fundus camera (FC) images. In this prospective, cross-sectional study, 94 consecutive patients in an urban eye emergency department underwent SO and FC fundus imaging via one of three study arms: medical student 1 (MS1), medical student 2 (MS2), and ophthalmology resident (OR). Images of 188 eyes were graded for overall quality by two masked reviewers, and observed critical fundus findings were compared to dilated fundus examination documentation. SO images were higher quality in the OR arm than in the MS1 and MS2 arms (P < .017). There were no differences in FC image quality between photographers (all P > .328). In the OR arm, SO images detected 74.3% of critical fundus findings, whereas FC images detected 77.1%. SO produces fundus images approaching the quality and diagnostic utility of traditional FC photographs.

An algorithm for the fast registration of image sequences obtained with a scanning laser ophthalmoscope

There is a need to quantify images of retinal pathology so that the natural progression and response to various forms of treatment such as laser therapy can be reliably monitored. The images obtained with a scanning laser ophthalmoscope are better than those from a traditional fundus camera, and the method of data collection leads naturally to digitization and quantitative techniques. However, during any angiographic sequence, there will inevitably be eye movement and it is essential that this be corrected for prior to the application of quantitative analysis. This article presents an algorithm (the smart localized binary correlation algorithm) for the automatic registration of fluorescein or indocyanine green angiograms which is based on localized correlation of binary templates in binary thresholded gradient images. The use of this algorithm decreases the calculation time up to 180 times when compared with the fastest applicable algorithm found in the literature. The algorithm only detects translational movement of the images but proves to be robust against rotational movement for the small angles (<4 degrees ) encountered in ophthalmic imaging. Registration accuracy is estimated to be +or-2 pixels.