Smartphone-based Fundus Camera Research Articles

Image Stitching of Low-Resolution Retinography Using Fundus Blur Filter and Homography Convolutional Neural Network

Great advances in stitching high-quality retinal images have been made in recent years. On the other hand, very few studies have been carried out on low-resolution retinal imaging. This work investigates the challenges of low-resolution retinal images obtained by the D-EYE smartphone-based fundus camera. The proposed method uses homography estimation to register and stitch low-quality retinal images into a cohesive mosaic. First, a Siamese neural network extracts features from a pair of images, after which the correlation of their feature maps is computed. This correlation map is fed through four independent CNNs to estimate the homography parameters, each specializing in different corner coordinates. Our model was trained on a synthetic dataset generated from the Microsoft Common Objects in Context (MSCOCO) dataset; this work added an important data augmentation phase to improve the quality of the model. Then, the same is evaluated on the FIRE retina and D-EYE datasets for performance measurement using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The obtained results are promising: the average PSNR was 26.14 dB, with an SSIM of 0.96 on the D-EYE dataset. Compared to the method that uses a single neural network for homography calculations, our approach improves the PSNR by 7.96 dB and achieves a 7.86% higher SSIM score.

Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy.

This study aimed to determine the generalizability of an artificial intelligence (AI) algorithm trained on an ethnically diverse dataset to screen for referable diabetic retinopathy (RDR) in the Armenian population unseen during AI development. This study comprised 550 patients with diabetes mellitus visiting the polyclinics of Armenia over 10 months requiring diabetic retinopathy (DR) screening. The Medios AI-DR algorithm was developed using a robust, diverse, ethnically balanced dataset with no inherent bias and deployed offline on a smartphone-based fundus camera. The algorithm here analyzed the retinal images captured using the target device for the presence of RDR (i.e., moderate non-proliferative diabetic retinopathy (NPDR) and/or clinically significant diabetic macular edema (CSDME) or more severe disease) and sight-threatening DR (STDR, i.e., severe NPDR and/or CSDME or more severe disease). The results compared the AI output to a consensus or majority image grading of three expert graders according to the International Clinical Diabetic Retinopathy severity scale. On 478 subjects included in the analysis, the algorithm achieved a high classification sensitivity of 95.30% (95% CI: 91.9%-98.7%) and a specificity of 83.89% (95% CI: 79.9%-87.9%) for the detection of RDR. The sensitivity for STDR detection was 100%. The study proved that Medios AI-DR algorithm yields good accuracy in screening for RDR in the Armenian population. In our literature search, this is the only smartphone-based, offline AI model validated in different populations.

Wide-field imaging with smartphone based fundus camera: grading of severity of diabetic retinopathy and locating peripheral lesions in diabetic retinopathy

AimTo assess the performance of smartphone based wide-field retinal imaging (WFI) versus ultra-wide-field imaging (UWFI) for assessment of sight-threatening diabetic retinopathy (STDR) as well as locating predominantly peripheral lesions (PPL) of DR.MethodsIndividuals with type 2 diabetes with varying grades of DR underwent nonmydriatic UWFI with Daytona Plus camera followed by mydriatic WFI with smartphone-based Vistaro camera at a tertiary care diabetes centre in South India in 2021–22. Grading of DR as well as identification of PPL (DR lesions beyond the posterior pole) in the retinal images of both cameras was performed by senior retina specialists. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The sensitivity and specificity of smartphone based WFI for detection of PPL and STDR was assessed. Agreement between the graders for both cameras was compared.ResultsRetinal imaging was carried out in 318 eyes of 160 individuals (mean age 54.7 ± 9 years; mean duration of diabetes 16.6 ± 7.9 years). The sensitivity and specificity for detection of STDR by Vistaro camera was 92.7% (95% CI 80.1–98.5) and 96.6% (95% CI 91.5–99.1) respectively and 95.1% (95% CI 83.5–99.4) and 95.7% (95% CI 90.3–98.6) by Daytona Plus respectively. PPL were detected in 89 (27.9%) eyes by WFI by Vistaro camera and in 160 (50.3%) eyes by UWFI. However, this did not translate to any significant difference in the grading of STDR between the two imaging systems. In both devices, PPL were most common in supero-temporal quadrant (34%). The prevalence of PPL increased with increasing severity of DR with both cameras (p < 0.001). The kappa comparison between the 2 graders for varying grades of severity of DR was 0.802 (p < 0.001) for Vistaro and 0.753 (p < 0.001) for Daytona Plus camera.ConclusionMydriatic smartphone-based widefield imaging has high sensitivity and specificity for detecting STDR and can be used to screen for peripheral retinal lesions beyond the posterior pole in individuals with diabetes.

Evaluation of an offline, artificial intelligence system for referable glaucoma screening using a smartphone-based fundus camera: a prospective study

Background/ObjectivesAn affordable and scalable screening model is critical for undetected glaucoma. The study evaluated the performance of an offline, smartphone-based AI system for the detection of referable glaucoma against two benchmarks: specialist diagnosis following full glaucoma workup and consensus image grading.Subjects/MethodsThis prospective study (tertiary glaucoma centre, India) included 243 subjects with varying severity of glaucoma and control group without glaucoma. Disc-centred images were captured using a validated smartphone-based fundus camera analysed by the AI system and graded by specialists. Diagnostic ability of the AI in detecting referable Glaucoma (Confirmed glaucoma) and no referable Glaucoma (Suspects and No glaucoma) when compared to a final diagnosis (comprehensive glaucoma workup) and majority grading (image grading) by Glaucoma specialists (pre-defined criteria) were evaluated.ResultsThe AI system demonstrated a sensitivity and specificity of 93.7% (95% CI: 87.6–96.9%) and 85.6% (95% CI:78.6–90.6%), respectively, in the detection of referable glaucoma when compared against final diagnosis following full glaucoma workup. True negative rate in definite non-glaucoma cases was 94.7% (95% CI: 87.2–97.9%). Amongst the false negatives were 4 early and 3 moderate glaucoma. When the same set of images provided to the AI was also provided to the specialists for image grading, specialists detected 60% (67/111) of true glaucoma cases versus a detection rate of 94% (104/111) by the AI.ConclusionThe AI tool showed robust performance when compared against a stringent benchmark. It had modest over-referral of normal subjects despite being challenged with fundus images alone. The next step involves a population-level assessment.

Accuracy of Low-Cost, Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening: A Systematic Review.

Diabetic retinopathy (DR) is a leading cause of blindness. Early DR screening is essential, but the infrastructure can be less affordable in low resource countries. This study aims to review the accuracy of low-cost smartphone-based fundus cameras for DR screening in adult patients with diabetes. We performed a systematic literature search to find studies that reported the sensitivity and specificity of low-cost smartphone-based devices for fundus photography in adult patients with diabetes. We searched three databases (MEDLINE, Google Scholar, Scopus) and one register (Cochrane CENTRAL). We presented the accuracy values by grouping the diagnosis into three: any DR, referrable DR, and diabetic macular oedema (DMO). Risk of bias and applicability of the studies were assessed using QUADAS-2. Five out of 294 retrieved records were included with a total of six smartphone-based devices reviewed. All of the reference diagnostic methods used in the included studies were either indirect ophthalmoscopy or slit-lamp examinations and all smartphone-based devices' imaging protocols used mydriatic drops. The reported sensitivity and specificity for any DR were 52-92.2% and 73.3-99%; for referral DR were 21-91.4% and 64.9-100%; and for DMO were 29.4-81% and 95-100%, respectively. Sensitivity available low-cost smartphone-based devices for DR screening were acceptable and their specificity particularly for detecting referrable DR and DMO were considerably good. These findings support their potential utilization for DR screening in a low resources setting.

Design of illumination system using characterized illuminances for smartphone-based fundus camera

Recently, smartphone-based fundus camera (SBFC) research has been actively conducted in response to the need to expand medical infrastructure in underdeveloped countries and the increased telemedicine since the COVID-19 pandemic. Compared to the conventional table-top system, SBFCs have technical challenges that make it difficult to guarantee uniform illumination and avoid back-reflection because of the design constraints of minimizing the form factor and cost. This paper proposes a novel illumination design methodology using characterized illuminance to obtain high-quality fundus images for SBFCs. Key performance indicators (KPIs), such as retinal uniformity, back-reflection suppression, and optical efficiency, were defined to evaluate the performance of the illumination system. Each KPI was calculated using optical simulation software based on Monte-Carlo ray tracing and mapped into a normalized three-dimensional coordinate, the retinal illumination performance space (RIPS). In RIPS, the KPIs are combined into a single parameter ΔRIPS, which is the quantitative difference evaluated as the Euclidean distance between the ideal and actual design point. A compact SBFC illumination system with five design variables was presented to verify the proposed methodology. The final design values at the minimum ΔRIPS were determined using the Taguchi method and response surface methodology. Finally, a working prototype was built, and fundus images were acquired by clinical testing under institutional review board approval. The fundus image had sufficient brightness and resolution to diagnose the lesion with a viewing angle of approximately 50° in one shot.

Agreement of a Novel Artificial Intelligence Software With Optical Coherence Tomography and Manual Grading of the Optic Disc in Glaucoma.

The offline artificial intelligence (AI) on a smartphone-based fundus camera shows good agreement and correlation with the vertical cup-to-disc ratio (vCDR) from the spectral-domain optical coherence tomography (SD-OCT) and manual grading by experts. The purpose of this study is to assess the agreement of vCDR measured by a new AI software from optic disc images obtained using a validated smartphone-based imaging device, with SD-OCT vCDR measurements, and manual grading by experts on a stereoscopic fundus camera. In a prospective, cross-sectional study, participants above 18 years (Glaucoma and normal) underwent a dilated fundus evaluation, followed by optic disc imaging including a 42-degree monoscopic disc-centered image (Remidio NM-FOP-10), a 30-degree stereoscopic disc-centered image (Kowa nonmyd WX-3D desktop fundus camera), and disc analysis (Cirrus SD-OCT). Remidio FOP images were analyzed for vCDR using the new AI software, and Kowa stereoscopic images were manually graded by 3 fellowship-trained glaucoma specialists. We included 473 eyes of 244 participants. The vCDR values from the new AI software showed strong agreement with SD-OCT measurements [95% limits of agreement (LoA)=-0.13 to 0.16]. The agreement with SD-OCT was marginally better in eyes with higher vCDR (95% LoA=-0.15 to 0.12 for vCDR>0.8). Interclass correlation coefficient was 0.90 (95% CI, 0.88-0.91). The vCDR values from AI software showed a good correlation with the manual segmentation by experts (interclass correlation coefficient=0.89, 95% CI, 0.87-0.91) on stereoscopic images (95% LoA=-0.18 to 0.11) with agreement better for eyes with vCDR>0.8 (LoA=-0.12 to 0.08). The new AI software vCDR measurements had an excellent agreement and correlation with the SD-OCT and manual grading. The ability of the Medios AI to work offline, without requiring cloud-based inferencing, is an added advantage.

Accuracy of Remote Diagnosis of Acute Posterior Segment Pathology by Residents and Attendings Captured with a Smartphone and Standard 20/28D Lens.

Background and ObjectiveTo assess the use of a smartphone-based fundus camera in diagnosing acute posterior segment disease in the emergency room (ER) of an academic center, where standard fundus cameras are currently unavailable.Study Design/Patients and MethodsImages were obtained on 10 eyes with various types of posterior segment pathology. These were analyzed retrospectively by 35 reviewers (21 residents and 14 attending physicians). Accuracy of diagnosis was compared to in-office fundus photography.ResultsThe average probability of correct diagnosis was over 78%. There was no difference between resident and attending reviewers, except for one of the vitreous hemorrhage cases where residents performed better (p=0.039).ConclusionSmartphone-based cameras are effective in screening for ocular diseases in the ER of academic centers, where residents and fellows are often the first providers to see various types of vision and life-threatening pathology.

Design of Illumination System Using Characterized Illuminances for Smartphone-Based Fundus Camera

Design of Illumination System Using Characterized Illuminances for Smartphone-Based Fundus Camera

Use of offline artificial intelligence in a smartphone-based fundus camera for community screening of diabetic retinopathy.

Purpose:The aim of the study was to analyse the reliability of an offline artificial intelligence (AI) algorithm for community screening of diabetic retinopathy.Methods:A total of 1378 patients with diabetes visiting public dispensaries under the administration of the Municipal Corporation of Greater Mumbai between August 2018 and September 2019 were enrolled for the study. Fundus images were captured by non-specialist operators using a smartphone-based camera covering the posterior pole, including the disc and macula, and the nasal and temporal fields. The offline AI algorithm on the smartphone marked the images as referable diabetic retinopathy (RDR) or non-RDR, which were then compared against the grading by two vitreoretinal surgeons to derive upon the sensitivity and specificity of the algorithm.Results:Out of 1378 patients, gradable fundus images were obtained and analysed for 1294 patients. The sensitivity and specificity of diagnosing RDR were 100% (95% CI: 94.72–100.00%) and 89.55% (95% CI: 87.76–91.16%), respectively; the same values for any diabetic retinopathy (DR) were 89.13% (95% CI: 82.71–93.79%) and 94.43% (95% CI: 91.89–94.74%), respectively, with no false-negative results.Conclusion:The robustness of the offline AI algorithm was established in this study making it a reliable tool for community-based DR screening.

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.

Review of retinal cameras for global coverage of diabetic retinopathy screening.

The global burden of diabetes has resulted in an increase in the prevalence of diabetic retinopathy (DR), a microvascular complication of diabetes. Lifelong repetitive screening for DR is essential for early detection and timely management to prevent visual impairment due to the silent sight-threatening disorder. Colour fundus photography (CFP) is helpful for documentation of the retinopathy as well as for counselling the patient. CFP has established roles in DR screening, detection, progression and monitoring of treatment response. DR screening programmes use validated mydriatic or non-mydriatic fundus cameras for retinal imaging and trained image graders identify referable DR. Smartphone-based fundus cameras and handheld fundus cameras that are cost-effective, portable and easy to handle in remote places are gaining popularity in recent years. The images captured with these low-cost devices can be immediately sent to trained ophthalmologists for grading of DR. Recent increase in numbers of telemedicine programmes based on imaging with digital fundus cameras and remote interpretation has facilitated larger population coverage of DR screening and timely referral of those with sight-threatening DR to ophthalmologists. Good-quality retinal imaging and accurate diagnosis are essential to reduce inappropriate referrals. Advances in digital imaging such as ultra-wide field imaging and multi-modal imaging have opened new avenues for assessing DR. Fundus cameras with integrated artificial intelligence (AI)-based automated algorithms can also provide instant DR diagnosis and reduce the burden of healthcare systems. We review the different types of fundus cameras currently used in DR screening and management around the world.

Medios- An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy.

Purpose:An observational study to assess the sensitivity and specificity of the Medios smartphone-based offline deep learning artificial intelligence (AI) software to detect diabetic retinopathy (DR) compared with the image diagnosis of ophthalmologists.Methods:Patients attending the outpatient services of a tertiary center for diabetes care underwent 3-field dilated retinal imaging using the Remidio NM FOP 10. Two fellowship-trained vitreoretinal specialists separately graded anonymized images and a patient-level diagnosis was reached based on grading of the worse eye. The images were subjected to offline grading using the Medios integrated AI-based software on the same smartphone used to acquire images. The sensitivity and specificity of the AI in detecting referable DR (moderate non-proliferative DR (NPDR) or worse disease) was compared to the gold standard diagnosis of the retina specialists.Results:Results include analysis of images from 297 patients of which 176 (59.2%) had no DR, 35 (11.7%) had mild NPDR, 41 (13.8%) had moderate NPDR, and 33 (11.1%) had severe NPDR. In addition, 12 (4%) patients had PDR and 36 (20.4%) had macular edema. Sensitivity and specificity of the AI in detecting referable DR was 98.84% (95% confidence interval [CI], 97.62–100%) and 86.73% (95% CI, 82.87–90.59%), respectively. The area under the curve was 0.92. The sensitivity for vision-threatening DR (VTDR) was 100%.Conclusion:The AI-based software had high sensitivity and specificity in detecting referable DR. Integration with the smartphone-based fundus camera with offline image grading has the potential for widespread applications in resource-poor settings.

Sensitivity and Specificity of Smartphone-Based Retinal Imaging for Diabetic Retinopathy: A Comparative Study

To determine the sensitivity and specificity of a smartphone-based fundus camera, the Remidio Fundus on Phone (FOP; Remidio Innovative Solutions Pvt. Ltd., Bengaluru, India) in detecting diabetic retinopathy (DR) compared with a conventional tabletop fundus camera and clinical examination. Cross-sectional, single-site, instrument validation study. Consecutive patients with diabetes who had no DR (n= 55 eyes), mild to moderate nonproliferative diabetic retinopathy (NPDR; n= 70 eyes), severe NPDR (n= 46 eyes), proliferative diabetic retinopathy (PDR; n = 62 eyes), and diabetic macular edema (DME; n= 44 eyes). All participants underwent a dilated examination to determine the grade of DR. Then all participants had mydriatic 45° fundus photographs obtained from three fields of view with the Remidio FOP and a Topcon tabletop fundus camera (Topcon Medical Systems, Inc., Oakland, NJ). Two masked retina specialists graded images for DR and DME, using National Health Service guidelines as well as for image quality using predefined criteria. Sensitivity and specificity of the Remidio FOP for the detection of any DR compared to clinical examination. One hundred thirty-five subjects (233 eyes) were recruited for the study. Compared with the reference clinical examination, using images from the Remidio FOP, graders 1 and 2 reported a sensitivity of 93.1% (95% confidence interval [CI]= 88.3-96.4) and 94.3% (95% CI= 89.7-97.2) and a specificity of 89.1% (95% CI= 68.2-92.2) and 94.5% (95% CI= 84.9-98.9), respectively, in identifying any DR (κ= 0.55; 95% CI=0.50-0.57). With images from the Topcon camera, graders reported similar sensitivities and specificities with marginally better agreement (κ= 0.68; 95% CI= 0.67-0.73). The sensitivity of detecting DR gradually increased from R1 to R3 disease using both cameras. Both graders classified significantly fewer images as "ungradable" (2.6%-4.3% for Topcon vs. 1.7%-2.1% for Remidio FOP) and more images as excellent from the Remidio FOP (59%-74%) than the Topcon device (52%-61%). The Remidio FOP device was found to have high sensitivity and specificity for the detection of any grade of DR with good agreement between graders. The rate of ungradable images was acceptably low and image quality was marginally better with the Remidio FOP.

Utility of smartphone-based fundus camera device in a social outreach setting

Context: Fundus photography. Aim: To demonstrate the use of smartphone-based fundus camera device for detecting retinal pathology in social outreach camps. Settings and Design: Prospective comparative study. Subjects and Methods: Two investigators were recruited, one trained in conventional indirect ophthalmoscopy and the other in smartphone-based fundus photography. A smartphone and a 20D lens were used to record the fundus in video mode. Thirty-six patients having nonproliferative diabetic retinopathy were included in the study and examined by both the investigators. Clinical staging done by them on the basis of their individual photographs were compared. Statistical Analysis Used: Chi-square test. Results: Smartphone-based fundus photography was able to capture quality images of the retina. The investigators were able to deduce the same clinical staging for 33 out of 36 patients (P = 0.02). Conclusions: The images can be forwarded to the retina specialist via E-mail or digital messenger and opinion on management or referral can thus be sought. This will help bring specialized medical expertise to masses at remote places.

Do it yourself smartphone fundus camera - DIYretCAM.

This article describes the method to make a do it yourself smartphone-based fundus camera which can image the central retina as well as the peripheral retina up to the pars plana. It is a cost-effective alternative to the fundus camera.

Smartphone-based fundus camera device (MII Ret Cam) and technique with ability to image peripheral retina.

To demonstrate an inexpensive smartphone-based fundus camera device (MII Ret Cam) and technique with ability to capture peripheral retinal pictures. A fundus camera was designed in the form of a device that has slots to fit a smartphone (built-in camera and flash) and 20-D lens. With the help of the device and an innovative imaging technique, high-quality fundus videos were taken with easy extraction of images. The MII Ret Cam and innovative imaging technique was able to capture high-quality images of peripheral retina such as ora serrata and pars plana apart from central fundus pictures. Our smartphone-based fundus camera can help clinicians to monitor diseases affecting both central and peripheral retina. It can help patients understand their disease and clinicians convincing their patients regarding need of treatment especially in cases of peripheral lesions. Imaging peripheral retina has not been demonstrated in existing smartphone-based fundus imaging techniques. The device can also be an inexpensive tool for mass screening.