Comparison of smartphone-based retinal imaging systems for diabetic retinopathy detection using deep learning

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors

The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on glucose series data from the wearable continuous glucose monitoring system. Therefore, this study developed a novel method, i.e., double deep latent autoencoder, for exploring glycemic variability influence from multi-day glucose data for diabetic retinopathy. Specifically, the model proposed in this research could encode continuous glucose sensor data with non-continuous and variable length via the integration of a data reorganization module and a novel encoding module with fragmented-missing-wise objective function. Additionally, the model implements a double deep autoencoder, which integrated convolutional neural network, long short-term memory, to jointly capturing the inter-day and intra-day glucose latent features from glucose series. The effectiveness of the proposed model is evaluated through a cross-validation method to clinical datasets of 765 type 2 diabetes patients. The proposed method achieves the highest accuracy value (0.89), precision value (0.88), and F1 score (0.73). The results suggest that our model can be used to remotely diagnose and screen for diabetic retinopathy by learning potential features of glucose series data collected by wearable continuous glucose monitoring systems.

Foundation model-driven distributed learning for enhanced retinal age prediction

OBJECTIVES

The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction.

MATERIALS AND METHODS

The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predicting retinal age. The framework explores federated learning (FL) as well as traveling model (TM) approaches for distributed training of the linear regression head. To evaluate this framework, we simulate a client network using fundus image data from the UK Biobank. Additionally, data from patients with type 1 diabetes from the UK Biobank and the Brazilian Multilabel Ophthalmological Dataset (BRSET) were utilized to explore the clinical utility of the developed methods.

RESULTS

Our findings reveal that the developed distributed learning framework achieves retinal age prediction performance on par with centralized methods, with FL and TM providing similar performance (mean absolute error of 3.57 ± 0.18 years for centralized learning, 3.60 ± 0.16 years for TM, and 3.63 ± 0.19 years for FL). Notably, the TM was found to converge with fewer local updates than FL. Moreover, patients with type 1 diabetes exhibited significantly higher RAG values than healthy controls in all models, for both the UK Biobank and BRSET datasets (P < .001).

DISCUSSION

The high computational and memory efficiency of the developed distributed learning framework makes it well suited for resource-constrained environments.

CONCLUSION

The capacity of this framework to integrate data from underrepresented populations for training of retinal age prediction models could significantly enhance the accessibility of the RAG as an important disease biomarker.

Development of 3D-printed universal adapter in enhancing retinal imaging accessibility

BACKGROUND

The revolutionary technology of smartphone-based retinal imaging has been consistently improving over the years. Smartphone-based retinal image acquisition devices are designed to be portable, easy to use, and cost-efficient, which enables eye care to be more widely accessible especially in geographically remote areas. This enables early disease detection for those who are in low- and middle- income population or just in general has very limited access to eye care. This study investigates the limitation of smartphone compatibility of existing smartphone-based retinal image acquisition devices. Additionally, this study aims to propose a universal adapter design that is usable with an existing smartphone-based retinal image acquisition device known as the PanOptic ophthalmoscope. This study also aims to simulate the reliability, validity, and performance overall of the developed prototype.

METHODS

A literature review has been conducted that identifies the limitation of smartphone compatibility among existing smartphone-based retinal image acquisition devices. Designing and modeling of proposed adapter were performed using the software AutoCAD 3D. For the proposed performance evaluation, finite element analysis (FEA) in the software Autodesk Inventor and 5-point scale method were demonstrated.

RESULTS

Published studies demonstrate that most of the existing smartphone-based retinal imaging devices have compatibility limited to specific older smartphone models. This highlights the benefit of a universal adapter in broadening the usability of existing smartphone-based retinal image acquisition devices. A functional universal adapter design has been developed that demonstrates its compatibility with a variety of smartphones regardless of the smartphone dimension or the position of the smartphone's camera lens. The proposed performance evaluation method generates an efficient stress analysis of the proposed adapter design. The end-user survey results show a positive overall performance of the developed universal adapter. However, a significant difference between the expert's views on the developed adapter and the quality of images is observed.

CONCLUSION

The compatibility of existing smartphone-based retinal imaging devices is still mostly limited to specific smartphone models. Besides this, the concept of a universal and suitable adapter for retinal imaging using the PanOptic ophthalmoscope was presented and validated in this paper. This work provides a platform for future development of smartphone-based ophthalmoscope that is universal.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

A novel deep learning model for diabetic retinopathy detection in retinal fundus images using pre-trained CNN and HWBLSTM

Diabetic retinopathy (DR) is a global visual indicator of diabetes that leads to blindness and loss of vision. Manual testing presents a more difficult task when attempting to detect DR due to the complexity and variances of DR. Early detection and treatment prevent the diabetic patients from visual loss. Also classifying the intensity and levels of DR is crucial to provide necessary treatment. This study develops a novel deep learning (DL) approach called He Weighted Bi-directional Long Short-term Memory (HWBLSTM) with an effective transfer learning technique for detecting DR from the RFI. The collected fundus images initially undergo preprocessing to improve their quality, which includes noise removal and contrast enhancement using a Hybrid Gaussian Filter and probability density Function-based Gamma Correction (HGFPDFGC) technique. The segmentation procedure divides the image into subgroups and is crucial for accurate detection and classification. The segmentation of the study initially removes the optical disk (OD) and blood vessels (BVs) from the preprocessed images using mathematical morphological operations. Next, it segments the retinal lesions from the OD and BV removed images using the Enhanced Grasshopper Optimization-based Region Growing Algorithm (EGORGA). Then, the features from the segmented retinal lesions are learned using a Squeeze Net (SQN), and the dimensionality reduction of the extracted features is done using the Modified Singular Value Decomposition (MSVD) approach. Finally, the classification is performed by employing the HWBLSTM approach, which classifies the DR abnormalities in datasets as non-DR (NDR), non-proliferative DR (NPDR), moderate NPDR (MDNPDR), and severe DR, also known as proliferative DR (PDR). The proposed approach is implemented on APTOS as well as MESSIDOR datasets. The outcomes proved that the proposed technique accurately identifies the DR with minimal computation overhead compared to the existing approaches.Communicated by Ramaswamy H. Sarma.

Clinical utility of handheld fundus and smartphone-based camera for monitoring diabetic retinal diseases: a review study

Diabetic retinopathy (DR) is the leading global cause of vision loss, accounting for 4.8% of global blindness cases as estimated by the World Health Organization (WHO). Fundus photography is crucial in ophthalmology as a diagnostic tool for capturing retinal images. However, resource and infrastructure constraints limit access to traditional tabletop fundus cameras in developing countries. Additionally, these conventional cameras are expensive, bulky, and not easily transportable. In contrast, the newer generation of handheld and smartphone-based fundus cameras offers portability, user-friendliness, and affordability. Despite their potential, there is a lack of comprehensive review studies examining the clinical utilities of these handheld (e.g. Zeiss Visuscout 100, Volk Pictor Plus, Volk Pictor Prestige, Remidio NMFOP, FC161) and smartphone-based (e.g. D-EYE, iExaminer, Peek Retina, Volk iNview, Volk Vistaview, oDocs visoScope, oDocs Nun, oDocs Nun IR) fundus cameras. This review study aims to evaluate the feasibility and practicality of these available handheld and smartphone-based cameras in medical settings, emphasizing their advantages over traditional tabletop fundus cameras. By highlighting various clinical settings and use scenarios, this review aims to fill this gap by evaluating the efficiency, feasibility, cost-effectiveness, and remote capabilities of handheld and smartphone fundus cameras, ultimately enhancing the accessibility of ophthalmic services.

Present and future screening programs for diabetic retinopathy: a narrative review

Diabetes is a prevalent global concern, with an estimated 12% of the global adult population affected by 2045. Diabetic retinopathy (DR), a sight-threatening complication, has spurred diverse screening approaches worldwide due to advances in DR knowledge, rapid technological developments in retinal imaging and variations in healthcare resources.Many high income countries have fully implemented or are on the verge of completing a national Diabetic Eye Screening Programme (DESP). Although there have been some improvements in DR screening in Africa, Asia, and American countries further progress is needed. In low-income countries, only one out of 29, partially implemented a DESP, while 21 out of 50 lower-middle-income countries have started the DR policy cycle. Among upper-middle-income countries, a third of 59 nations have advanced in DR agenda-setting, with five having a comprehensive national DESP and 11 in the early stages of implementation.Many nations use 2-4 fields fundus images, proven effective with 80-98% sensitivity and 86-100% specificity compared to the traditional seven-field evaluation for DR. A cell phone based screening with a hand held retinal camera presents a potential low-cost alternative as imaging device. While this method in low-resource settings may not entirely match the sensitivity and specificity of seven-field stereoscopic photography, positive outcomes are observed.Individualized DR screening intervals are the standard in many high-resource nations. In countries that lacks a national DESP and resources, screening are more sporadic, i.e. screening intervals are not evidence-based and often less frequently, which can lead to late recognition of treatment required DR.The rising global prevalence of DR poses an economic challenge to nationwide screening programs AI-algorithms have showed high sensitivity and specificity for detection of DR and could provide a promising solution for the future screening burden.In summary, this narrative review enlightens on the epidemiology of DR and the necessity for effective DR screening programs. Worldwide evolution in existing approaches for DR screening has showed promising results but has also revealed limitations. Technological advancements, such as handheld imaging devices, tele ophthalmology and artificial intelligence enhance cost-effectiveness, but also the accessibility of DR screening in countries with low resources or where distance to or a shortage of ophthalmologists exists.

Smartphone Eye Examination: Artificial Intelligence and Telemedicine

Background: The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in particular, it contributes greatly to screening diseases with high rates of blindness. Eye examination using smartphones essentially represents a cheap and safe method, thus contributing to public policies on population screening. This review aims to provide an update on the use of this resource and its future prospects, especially as a screening and ophthalmic diagnostic tool. Methods: In this review, we surveyed major published advances in retinal and anterior segment analysis using AI. We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for published literature without a deadline. We included studies that compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting prevalent diseases with an accurate or commonly employed reference standard. Results: There are few databases with complete metadata, providing demographic data, and few databases with sufficient images involving current or new therapies. It should be taken into consideration that these are databases containing images captured using different systems and formats, with information often being excluded without essential detailing of the reasons for exclusion, which further distances them from real-life conditions. The safety, portability, low cost, and reproducibility of smartphone eye images are discussed in several studies, with encouraging results. Conclusions: The high level of agreement between conventional and a smartphone method shows a powerful arsenal for screening and early diagnosis of the main causes of blindness, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration. In addition to streamlining the medical workflow and bringing benefits for public health policies, smartphone eye examination can make safe and quality assessment available to the population.

A Systematic Review of Digital Ophthalmoscopes in Medicine

Purpose

Recent advances in telemedicine have led to increased use of digital ophthalmoscopes (DO) in clinical settings. This review aims to assess commercially available DOs, including smartphone (SP), desktop, and handheld ophthalmoscopes, and evaluate their applications.

Methods

A literature review was performed by searching PubMed (pubmed.ncbi.nlm.nih.gov), Web of Science (webofknowledge.com), and Science Direct (sciencedirect.com). All English-language papers that resulted from the search terms "digital ophthalmoscope", "screening tool", "glaucoma screening", "diabetic retinopathy screening", "cataract screening", and "papilledema screening" were reviewed. Studies that contained randomized clinical trials with human participants between January 2010 and December 2020 were included. The Risk of Bias in Systematic Reviews (ROBIS) tool was used to assess the methodological quality of each included paper.

Results

Of the 1307 studies identified, 35 met inclusion and exclusion criteria. The ROBIS tool determined that 29/35 studies (82.8%) had a low risk of bias, 3/35 (8.5%) had a moderate risk of bias, and 3/35 (8.5%) had a high risk of bias.

Conclusion

The continued adoption of DOs remains uncertain because of concerns about the image quality for non-mydriatic eyes and the confidence in data captured from the device. Likewise, there is a lack of guidelines for the use of DOs, which makes it difficult for providers to determine the best device for their practice and to ensure appropriate use. Even so, DOs continue to gain acceptance as technology and practice integration improve, especially in underserved areas with limited access to ophthalmologists.

Diabetes more than retinopathy, it’s effect on the anterior segment of eye

Diabetes mellitus (DM) is one of the chronic metabolic noncommunicable diseases that has attained worldwide epidemics. It threatens healthy life around the globe, with mild-to-severe secondary complications and leads to significant illness including nephropathy, neuropathy, retinopathy, and macrovascular abnormalities including peripheral vasculopathy, and ischaemic heart disease. Research into diabetic retinopathy (DR), which affects one-third of persons with diabetes, has made considerable strides in recent years. In addition, it can lead to several anterior segment complications such as glaucoma, cataract, cornea, conjunctiva, lacrimal glands and other ocular surface diseases. Uncontrolled DM also caused gradual damage to corneal nerves and epithelial cells, which raises the likelihood of anterior segment diseases including corneal ulcers, dry eye disease, and chronic epithelial abnormalities. Although DR and other associated ocular complications are well-known, the complexity of its aetiology and diagnosis makes therapeutic intervention challenging. Strict glycaemic control, early detection and regular screening, and meticulous management is the key to halting the progression of the disease. In this review manuscript, we aim to provide an in-depth understanding of the broad spectrum of diabetic complications in the anterior segment of the ocular tissues and illustrate the progression of diabetes and its pathophysiology, epidemiology, and prospective therapeutic targets. This first such review article will highlight the role of diagnosing and treating patients with a plethora of anterior segment diseases associated with diabetes, which are often neglected.

Evaluation of a portable retinal imaging device: towards a comparative quantitative analysis for morphological measurements of retinal blood vessels

This study investigated the possibility of using low-cost, handheld, retinal imaging devices for the automatic extraction of quantifiable measures of retinal blood vessels. Initially, the available handheld devices were compared using a Zeiss model eye incorporating a USAF resolution test chart to assess their optical properties. The only suitable camera of the five evaluated was the Horus DEC 200. This device was then subjected to a detailed evaluation in which images in human eyes taken from the handheld camera were compared in a quantitative analysis with those of the same eye from a Canon CR-DGi retinal desktop camera. We found that the Horus DEC 200 exhibited shortcomings in capturing images of human eyes by comparison with the Canon. More images were rejected as being unevaluable or suffering failures in automatic segmentation than with the Canon, and even after exclusion of affected images, the Horus yielded lower measurements of vessel density than the Canon. A number of issues affecting handheld cameras in general and some features of the Horus in particular have been identified that might contribute to the observed differences in performance. Some potential mitigations are discussed which might yield improvements in performance, thus potentially facilitating use of handheld retinal imaging devices for quantitative retinal microvascular measurements.

A study establishing sensitivity and accuracy of smartphone photography in ophthalmologic community outreach programs: Review of a smart eye camera

Purpose

Diseases affecting the cornea are a major cause of corneal blindness globally. The pressing issue we are facing today is the lack of diagnostic devices in rural areas to diagnose these conditions. The aim of the study is to establish sensitivity and accuracy of smartphone photography using a smart eye camera (SEC) in ophthalmologic community outreach programs.

Methods

In this pilot study, a prospective non-randomized comparative analysis of inter-observer variability of anterior segment imaging recorded using an SEC was performed. Consecutive 100 patients with corneal pathologies, who visited the cornea specialty outpatient clinic, were enrolled. They were examined with a conventional non-portable slit lamp by a cornea consultant, and the diagnoses were recorded. This was compared with the diagnoses made by two other consultants based on SEC videos of the anterior segment of the same 100 patients. The accuracy of SEC was accessed using sensitivity, specificity, PPV, and NPV. Kappa statistics was used to find the agreement between two consultants by using STATA 17.0 (Texas, USA).

Results

There was agreement between the two consultants to diagnosing by using SEC. Above 90% agreements were found in all the diagnoses, which were statistically significant (P-value < 0.001). More than 90% sensitivity and a negative predictive value were found.

Conclusion

SEC can be used successfully in the community outreach programs like field visits, eye camps, teleophthalmology, and community centers, where either a clinical setup is lacking or ophthalmologists are not available.

Smartphone for retinal imaging - Case series in resource-limited rural settings

Purpose

There is a clinical need for a cost-effective, reliable, easy-to-use, and portable retinal photography. The use of smartphone fundus photography for documentation of retinal changes in resource-limited settings, where retinal imaging was not previously possible, is studied here. The introduction of smartphone-based retinal imaging has resulted in the increase in available technologies for fundus photography. On account of the cost, fundus cameras are not readily available in ophthalmic practice in developing countries. Because smartphones are readily available, easy to use, and also portable, they present a low-cost alternative method in resource-limited settings. The aim is to explore the use of smartphones (iphones) for retinal imaging in resource-limited settings.

Methods

A smartphone (iphone) was used to acquire retinal images with the use of +20 D lens in patients with dilated pupils by activating the video mode of the camera.

Results

Clear retinal images were obtained in different clinical conditions in adults and children, including branch retinal vein occlusion with fibro-vascular proliferation, choroidal neo-vascular membranes, presumed ocular toxoplasmosis, diabetic retinopathy, retinoblastoma, ocular albinism, and hypertensive retinopathy.

Conclusion

New inexpensive, portable, easy-to-operate cameras have revolutionized retinal imaging and screening programs and play an innovative role in research, education, and information sharing.

The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases

In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.

Use of a Smartphone-Based Device for Fundus Examination in Birds: A Pilot Study

Simple Summary

Eye examination is crucial for therapeutic plans and rehabilitation of birds in wildlife rehabilitation centers. However, fundus examination using classical direct or indirect ophthalmoscopy techniques can be challenging in those species. The aim of the study was to assess the use of a smartphone-based retinal imaging system in birds. Fundus examination was feasible in most bird species examined in this study. The difficulties of carrying out the examination seem to be related to the form of the globe, the color of the iris, and the quality of pupil dilation. Further investigations are necessary to confirm these findings.

Abstract

Ophthalmic examination is essential in the avian triage process in order to apply prompt therapeutic plans and evaluate rehabilitation potential. Fundoscopy is traditionally performed by direct or indirect ophthalmoscopy. Recent technological developments have enabled the design of a small-sized and affordable retinal imaging system to examine the fundus. We investigate the use of a smartphone-based device to realize fundus examination through a prospective cross-sectional observational study. Seventy-seven eyes of 39 birds of 15 different species were evaluated using the smartphone-based device in a rescue wildlife center. Pupil dilation was achieved prior to examination via rocuronium topical application. Assessment of fundus by the smartphone was classified as satisfactory, moderately satisfactory, and unsatisfactory. Fundus examination was also performed with a 20D, 30D, or 78D lens for comparison. Pupillary dilation was satisfactory, moderately satisfactory, or absent in 17, 32, and 28 eyes, respectively. Fundus examination with the smartphone-based device was satisfactory, moderately satisfactory, or unsatisfactory in 44, 15, and 18 eyes, respectively. The feasibility of the fundus examination was affected by the form of the globe; by the quality of pupil dilation; by the color of the iris (images could not be obtained from species with an orange, bright iris); and by the species, with owls (Strigiformes) being the easiest to observe. Based on these findings, fundus examination was feasible in most bird species examined in this study.

Collaborative Deep Learning for Privacy Preserving Diabetic Retinopathy Detection

Convolutional Neural Networks (CNNs) are an emerging research area for detection of Diabetic Retinopathy (DR) development in fundus images with highly reliable results. However, its accuracy depends on the availability of big datasets to train such a deep network. Due to the privacy concerns, the strict rules on medical data limit accessibility of images in publicly available datasets. In this paper, we propose a collaborative learning approach to train CNN models with multiple datasets while preserving the privacy of datasets in a distributed learning environment without sharing them. First, CNN networks are trained with private datasets, and tested with the same publicly available images. Based on their initial accuracies, the CNN model with the lowest performance among datasets is forwarded to second lowest performed dataset to retrain it using the transfer learning approach. Then, the retrained network is forwarded to next dataset. This procedure is repeated for each dataset from the lowest performed dataset to the highest. With this ascending chain order fashion, the network is retrained again and again using different datasets and its performance is improved over the time. Based on our experimental results on five different retina image datasets, DR detection accuracy is increased to 93.5% compared with the accuracies of merged datasets (84%) and individual datasets (73%, 78%, 83%, 85%).

Smartphone-Acquired Anterior Segment Images for Deep Learning Prediction of Anterior Chamber Depth: A Proof-of-Concept Study

Purpose

To develop a deep learning (DL) algorithm for predicting anterior chamber depth (ACD) from smartphone-acquired anterior segment photographs.

Methods

For algorithm development, we included 4,157 eyes from 2,084 Chinese primary school students (aged 11–15 years) from Mojiang Myopia Progression Study (MMPS). All participants had with ACD measurement measured with Lenstar (LS 900) and anterior segment photographs acquired from a smartphone (iPhone Xs), which was mounted on slit lamp and under diffuses lighting. The anterior segment photographs were randomly selected by person into training (80%, no. of eyes = 3,326) and testing (20%, no. of eyes = 831) dataset. We excluded participants with intraocular surgery history or pronounced corneal haze. A convolutional neural network was developed to predict ACD based on these anterior segment photographs. To determine the accuracy of our algorithm, we measured the mean absolute error (MAE) and coefficient of determination (R²) were evaluated. Bland Altman plot was used to illustrate the agreement between DL-predicted and measured ACD values.

Results

In the test set of 831 eyes, the mean measured ACD was 3.06 ± 0.25 mm, and the mean DL-predicted ACD was 3.10 ± 0.20 mm. The MAE was 0.16 ± 0.13 mm, and R² was 0.40 between the predicted and measured ACD. The overall mean difference was −0.04 ± 0.20 mm, with 95% limits of agreement ranging between −0.43 and 0.34 mm. The generated saliency maps showed that the algorithm mainly utilized central corneal region (i.e., the site where ACD is clinically measured typically) in making its prediction, providing further plausibility to the algorithm's prediction.

Conclusions

We developed a DL algorithm to estimate ACD based on smartphone-acquired anterior segment photographs. Upon further validation, our algorithm may be further refined for use as a ACD screening tool in rural localities where means of assessing ocular biometry is not readily available. This is particularly important in China where the risk of primary angle closure disease is high and often undetected.

EDR-Net: Lightweight Deep Neural Network Architecture for Detecting Referable Diabetic Retinopathy

Present architecture of convolution neural network for diabetic retinopathy (DR-Net) is based on normal convolution (NC). It incurs high computational cost as NC uses a multiplicative weight that measures a combined correlation in both cross-channel and spatial dimension of layer's inputs. This might cause the overall DR-Net architecture to be over-parameterised and computationally inefficient. This paper proposes EDR-Net - a new end-to-end, DR-Net architecture with depth-wise separable convolution module. The EDR-Net architecture was trained with DRKaggle-train dataset (35,126 images), and tested on two datasets, i.e. DRKaggle-test (53,576 images) and Messidor-2 (1,748 images). Results showed that the proposed EDR-Net achieved predictive performance comparable with current state-of-the-arts in detecting referable diabetic retinopathy (rDR) from fundus images and outperformed other light weight architectures, with at least two times less computation cost. This makes it more amenable for mobile device based computer-assisted rDR screening applications.

Diagnostic circulating biomarkers to detect vision-threatening diabetic retinopathy: Potential screening tool of the future?

With the increasing prevalence of diabetes in developing and developed countries, the socio-economic burden of diabetic retinopathy (DR), the leading complication of diabetes, is growing. Diabetic retinopathy (DR) is currently one of the leading causes of blindness in working-age adults worldwide. Robust methodologies exist to detect and monitor DR; however, these rely on specialist imaging techniques and qualified practitioners. This makes detecting and monitoring DR expensive and time-consuming, which is particularly problematic in developing countries where many patients will be remote and have little contact with specialist medical centres. Diabetic retinopathy (DR) is largely asymptomatic until late in the pathology. Therefore, early identification and stratification of vision-threatening DR (VTDR) is highly desirable and will ameliorate the global impact of this disease. A simple, reliable and more cost-effective test would greatly assist in decreasing the burden of DR around the world. Here, we evaluate and review data on circulating protein biomarkers, which have been verified in the context of DR. We also discuss the challenges and developments necessary to translate these promising data into clinically useful assays, to detect VTDR, and their potential integration into simple point-of-care testing devices.

Optimized hybrid machine learning approach for smartphone based diabetic retinopathy detection

Diabetic Retinopathy (DR) is defined as the Diabetes Mellitus difficulty that harms the blood vessels in the retina. It is also known as a silent disease and cause mild vision issues or no symptoms. In order to enhance the chances of effective treatment, yearly eye tests are vital for premature discovery. Hence, it uses fundus cameras for capturing retinal images, but due to its size and cost, it is a troublesome for extensive screening. Therefore, the smartphones are utilized for scheming low-power, small-sized, and reasonable retinal imaging schemes to activate automated DR detection and DR screening. In this article, the new DIY (do it yourself) smartphone enabled camera is used for smartphone based DR detection. Initially, the preprocessing like green channel transformation and CLAHE (Contrast Limited Adaptive Histogram Equalization) are performed. Further, the segmentation process starts with optic disc segmentation by WT (watershed transform) and abnormality segmentation (Exudates, microaneurysms, haemorrhages, and IRMA) by Triplet half band filter bank (THFB). Then the different features are extracted by Haralick and ADTCWT (Anisotropic Dual Tree Complex Wavelet Transform) methods. Using life choice-based optimizer (LCBO) algorithm, the optimal features are chosen from the mined features. Then the selected features are applied to the optimized hybrid ML (machine learning) classifier with the combination of NN and DCNN (Deep Convolutional Neural Network) in which the SSD (Social Ski-Driver) is utilized for the best weight values of hybrid classifier to categorize the severity level as mild DR, severe DR, normal, moderate DR, and Proliferative DR. The proposed work is simulated in python environment and to test the efficiency of the proposed scheme the datasets like APTOS-2019-Blindness-Detection, and EyePacs are used. The model has been evaluated using different performance metrics. The simulation results verified that the suggested scheme is provides well accuracy for each dataset than other current approaches.

[Ocular changes as a diagnostic tool for malaria]

BACKGROUND

According to the WHO Malaria Report 2019 a total of 229 million people fall ill with malaria each year and two thirds of deaths involve children under 5 years of age.

AIM

To review the fundus changes in the context of malaria and the importance of ophthalmoscopy in the diagnosis.

MATERIAL AND METHODS

Summary of changes in cerebral malaria visible on fundus examination, possible underlying pathomechanisms and the value of ophthalmoscopy in practice.

RESULTS

Retinal findings in malaria include white or gray staining of the retina (retinal whitening), color change of retinal vessels (orange or white staining), hemorrhages often with a white center, such as Roth's spot and papilledema.

DISCUSSION

The retinal changes in malaria are specific and may help to differentiate malaria from other causes of coma and fever. Smartphone-based fundus photography and artificial intelligence could support malaria diagnostics particularly in resource-poor regions.

[Smartphone-based fundus imaging: applications and adapters]

BACKGROUND

Smartphone-based fundus imaging (SBFI) is an innovative and low-cost alternative for color fundus photography. Since the first reports on this topic more than 10 years ago a large number of studies on different adapters and clinical applications have been published.

OBJECTIVE

The aim of this review article is to provide an overview on the development of SBFI and adapters and clinical applications published so far.

MATERIAL AND METHODS

A literature search was performed using the MEDLINE and Science Citation Index Expanded databases without time restrictions.

RESULTS

Overall, 11 adapters were included and compared in terms of exemplary image material, field of view, acquisition costs, weight, software, application range, smartphone compatibility and certification. Previously published SBFI applications are screening for diabetic retinopathy, glaucoma and retinopathy of prematurity as well as the application in emergency medicine, pediatrics and medical education/teaching. Image quality of conventional retinal cameras is in general superior to SBFI. First approaches on automatic detection of diabetic retinopathy through SBFI are promising and the use of automatic image processing algorithms enables the generation of wide-field image montages.

CONCLUSION

SBFI is a versatile, mobile, low-cost alternative to conventional equipment for color fundus photography. In addition, it facilitates the delegation of ophthalmological examinations to assistance personnel in telemedical settings, could simplify retinal documentation, improve teaching, and improve ophthalmological care, particularly in countries with low and middle incomes.

A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection

Purpose

Lens adapted smartphones are being used regularly instead of ophthalmoscopes. The most common causes of preventable blindness in the world, which are glaucoma and diabetic retinopathy, can develop asymptomatic changes to the optic nerve head (ONH) especially in the developing world where there is a dire shortage of ophthalmologists but ubiquitous mobile phones. We developed a proof-of-concept ONH biometric (application [APP]) to use as a routine biometric on a mobile phone. The unique blood vessel pattern is verified if it maps on to a previously enrolled image.

Methods

The iKey APP platform comprises three deep neural networks (DNNs) developed from anonymous ONH images: the graticule blood vessel (GBV) and the blood vessel specific feature (BVSF) DNNs were trained on unique blood vessel vectors. A non-feature specific (NFS) baseline ResNet50 DNN was trained for comparison.

Results

Verification reached an accuracy of 97.06% with BVSF, 87.24% with GBV and 79.8% using NFS.

Conclusions

A new ONH biometric was developed with a hybrid platform of ONH algorithms for use as a verification biometric on a smartphone. Failure to verify will alert the user to possible changes to the image, so that silent changes may be observed before sight threatening disease progresses. The APP retains a history of all ONH images. Future longitudinal analysis will explore the impact of ONH changes to the iKey biometric platform.

Translational Relevance

Phones with iKey will host ONH images for biometric protection of both health and financial data. The ONH may be used for automatic screening by new disease detection DNNs.

Smartphone fundus photography: a narrative review

Background

The idea to use smartphone for fundus photography was put forward in 2010. Over the last decade, there has been a dramatic development in this field. This narrative review focuses on the principle of smartphone fundus photography, how to master this technique, problems encountered by the beginners, camera applications/devices designed for this purpose and the safety profile of smartphone flashlights for retinal photoreceptors.

Discussion

Smartphone fundus photography using a condensing lens is based on the same principle as indirect ophthalmoscopy. Smartphone flashlight serves the purpose of light source or illuminating system. Real and inverted image of the retina is focused by the smartphone camera after adjustment of the filming distance. Beginners can face difficulties like adjustment of the filming distance, glare from condensing lens and reflection from the ceiling lights. Mobile camera applications and holding devices designed for this purpose can help the beginners to address these difficulties. There have been safety concerns about photo-biological risk for retinal photoreceptors by flashlight. Although the spectral irradiance on the retina, while using smartphone for fundus imaging is within the safety limits set by ISO 15004-2.2. The safety profile of latest model flashlights which deliver more power compared to older flashlights, need to be assessed.

Conclusion

Smartphone fundus photography is a cheap, cost effective, portable and a convenient method for retinal imaging. With practice and use of smartphone camera applications designed for this purpose, the beginners can master this technique. By training young ophthalmology residents and ophthalmic primary caretakers, this retinal imaging technique can be utilized for artificial intelligence, patient diagnostic and educational purposes.

Teaching Smartphone Funduscopy with 20 Diopter Lens in Undergraduate Medical Education

Purpose

To assess attitudes of pre-clinical undergraduate medical students toward learning smartphone funduscopy (SF) and its appropriateness as a teaching tool.

Patients and Methods

Second year medical students received instruction on direct ophthalmoscopy (DO) and SF; they were then paired with a peer and randomly assigned to perform DO or SF first. The SF technique involved freehand alignment of the axes of the smartphone camera with a condenser lens. Both techniques were done through a maximally dilated pupil. A questionnaire was completed to acquire data on baseline experience, performance of both examination techniques, attitudes, and appropriateness. Statistical significance testing and Bland-Altman analysis were used to determine differences between DO and SF, and a multivariable mixed regression model was fitted to identify any predictors for positive attitudes toward DO or SF.

Results

One hundred thirty-seven (137) individuals completed the study. A similar proportion of students could identify the optic nerve, macula, and vessels using DO and SF. However, self-reported quality scores were higher for DO for the optic nerve (p = 0.006) and macula (p = 0.08). The mean (standard deviation) attempts to identify these major structures were 2.7 (SD 2.3) for DO and 4.5 (SD 2.9) for SF (p < 0.001). Attitudes of students were consistently more positive toward DO across the five questions assessed. A small subset of students had equally positive attitudes toward DO and SF. Improved quality scores were predictive of positive attitudes for both DO and SF. Ultimately, 24% of students preferred SF over DO.

Conclusion

Among inexperienced examiners of the fundus through a dilated pupil, SF is a non-inferior technique to DO in identifying structures. Despite overall favorable attitudes towards the more familiar DO, those students who quickly learned the SF technique had similar satisfaction scores. Teaching SF should be considered in undergraduate medical education.