Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System

A Review of Advancements and Evidence Gaps in Diabetic Retinopathy Screening Models

Diabetic retinopathy (DR) is a microvascular complication of diabetes with a prevalence of ~35%, and is one of the leading causes of visual impairment in people of working age in most developed countries. The earliest stage of DR, non-proliferative DR (NPDR), may progress to sight-threatening DR (STDR). Thus, early detection of DR and active regular screening of patients with diabetes are necessary for earlier intervention to prevent sight loss. While some countries offer systematic DR screening, most nations are reliant on opportunistic screening or do not offer any screening owing to limited healthcare resources and infrastructure. Currently, retinal imaging approaches for DR screening include those with and without mydriasis, imaging in single or multiple fields, and the use of conventional or ultra-wide-field imaging. Advances in telescreening and automated detection facilitate screening in previously hard-to-reach communities. Despite the heterogeneity in approaches to fit local needs, an evidence base must be created for each model to inform practice. In this review, we appraise different aspects of DR screening, including technological advances, identify evidence gaps, and propose several studies to improve DR screening globally, with a view to identifying patients with moderate-to-severe NPDR who would benefit if a convenient treatment option to delay progression to STDR became available.

Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study

INTRODUCTION

Early screening for diabetic retinopathy (DR) with an efficient and scalable method is highly needed to reduce blindness, due to the growing epidemic of diabetes. The aim of the study was to validate an artificial intelligence-enabled DR screening and to investigate the prevalence of DR in adult patients with diabetes in China.

RESEARCH DESIGN AND METHODS

The study was prospectively conducted at 155 diabetes centers in China. A non-mydriatic, macula-centered fundus photograph per eye was collected and graded through a deep learning (DL)-based, five-stage DR classification. Images from a randomly selected one-third of participants were used for the DL algorithm validation.

RESULTS

In total, 47 269 patients (mean (SD) age, 54.29 (11.60) years) were enrolled. 15 805 randomly selected participants were reviewed by a panel of specialists for DL algorithm validation. The DR grading algorithms had a 83.3% (95% CI: 81.9% to 84.6%) sensitivity and a 92.5% (95% CI: 92.1% to 92.9%) specificity to detect referable DR. The five-stage DR classification performance (concordance: 83.0%) is comparable to the interobserver variability of specialists (concordance: 84.3%). The estimated prevalence in patients with diabetes detected by DL algorithm for any DR, referable DR and vision-threatening DR were 28.8% (95% CI: 28.4% to 29.3%), 24.4% (95% CI: 24.0% to 24.8%) and 10.8% (95% CI: 10.5% to 11.1%), respectively. The prevalence was higher in female, elderly, longer diabetes duration and higher glycated hemoglobin groups.

CONCLUSION

This study performed, a nationwide, multicenter, DL-based DR screening and the results indicated the importance and feasibility of DR screening in clinical practice with this system deployed at diabetes centers.

TRIAL REGISTRATION NUMBER

NCT04240652.

Application of non-mydriatic fundus examination and artificial intelligence to promote the screening of diabetic retinopathy in the endocrine clinic: an observational study of T2DM patients in Tianjin, China

Background

We aimed to determine the role of non-mydriatic fundus examination and artificial intelligence (AI) in screening diabetic retinopathy (DR) in patients with diabetes in the Metabolic Disease Management Center (MMC) in Tianjin, China.

Methods

Adult patients with type 2 diabetes mellitus who were first treated by MMC in Tianjin First Central Hospital and Tianjin 4th Center Hospital were divided into two groups according to the time that MMC was equipped with the non-mydriatic ophthalmoscope and AI system and could complete fundus examination independently (the former was the control group, the latter was the observation group). The observation indices were as follows: the incidence of DR, the fundus screening rate of the two groups, and fundus screening of diabetic patients with different course of disease.

Results

A total of 5039 patients were enrolled in this study. The incidence rate of DR was 18.6%, 29.8%, and 49.6% in patients with diabetes duration of ⩽1 year, 1-5 years, and >5 years, respectively. The screening rate of fundus in the observation group was significantly higher compared with the control group (81.3% versus 28.4%, χ ² = 1430.918, p < 0.001). The DR screening rate of the observation group was also significantly higher compared with the control group in patients with diabetes duration of ⩽1 year (77.3% versus 20.6%; χ ² = 797.534, p < 0.001), 1-5 years (82.5% versus 31.0%; χ ² = 197.124, p < 0.001) and ⩾5 years (86.9% versus 37.1%; χ² = 475.609, p < 0.001).

Conclusions

In the case of limited medical resources, MMC can carry out one-stop examination, treatment, and management of DR through non-mydratic fundus examination and AI assistance, thus incorporating the DR screening process into the endocrine clinic, so as to facilitate early diagnosis.

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.

Accelerating ophthalmic artificial intelligence research: the role of an open access data repository

PURPOSE OF REVIEW

Artificial intelligence has already provided multiple clinically relevant applications in ophthalmology. Yet, the explosion of nonstandardized reporting of high-performing algorithms are rendered useless without robust and streamlined implementation guidelines. The development of protocols and checklists will accelerate the translation of research publications to impact on patient care.

RECENT FINDINGS

Beyond technological scepticism, we lack uniformity in analysing algorithmic performance generalizability, and benchmarking impacts across clinical settings. No regulatory guardrails have been set to minimize bias or optimize interpretability; no consensus clinical acceptability thresholds or systematized postdeployment monitoring has been set. Moreover, stakeholders with misaligned incentives deepen the landscape complexity especially when it comes to the requisite data integration and harmonization to advance the field. Therefore, despite increasing algorithmic accuracy and commoditization, the infamous 'implementation gap' persists. Open clinical data repositories have been shown to rapidly accelerate research, minimize redundancies and disseminate the expertise and knowledge required to overcome existing barriers. Drawing upon the longstanding success of existing governance frameworks and robust data use and sharing agreements, the ophthalmic community has tremendous opportunity in ushering artificial intelligence into medicine. By collaboratively building a powerful resource of open, anonymized multimodal ophthalmic data, the next generation of clinicians can advance data-driven eye care in unprecedented ways.

SUMMARY

This piece demonstrates that with readily accessible data, immense progress can be achieved clinically and methodologically to realize artificial intelligence's impact on clinical care. Exponentially progressive network effects can be seen by consolidating, curating and distributing data amongst both clinicians and data scientists.

Interpretation of artificial intelligence studies for the ophthalmologist

PURPOSE OF REVIEW

The use of artificial intelligence (AI) in ophthalmology has increased dramatically. However, interpretation of these studies can be a daunting prospect for the ophthalmologist without a background in computer or data science. This review aims to share some practical considerations for interpretation of AI studies in ophthalmology.

RECENT FINDINGS

It can be easy to get lost in the technical details of studies involving AI. Nevertheless, it is important for clinicians to remember that the fundamental questions in interpreting these studies remain unchanged - What does this study show, and how does this affect my patients? Being guided by familiar principles like study purpose, impact, validity, and generalizability, these studies become more accessible to the ophthalmologist. Although it may not be necessary for nondomain experts to understand the exact AI technical details, we explain some broad concepts in relation to AI technical architecture and dataset management.

SUMMARY

The expansion of AI into healthcare and ophthalmology is here to stay. AI systems have made the transition from bench to bedside, and are already being applied to patient care. In this context, 'AI education' is crucial for ophthalmologists to be confident in interpretation and translation of new developments in this field to their own clinical practice.

The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence

As a third of people with diabetes mellitus (DM) will suffer the microvascular complications of diabetic retinopathy (DR) and therapeutic options can effectively prevent visual impairment, systematic screening has substantially reduced disease burden in developed countries. In an effort to tackle the rising incidence of DM, screening programmes have modernized in synchrony with technical and infrastructural advancements. Patient evaluation has shifted from face-to-face ophthalmologist-based review delivered through community grassroots to asynchronous store-and-forward modern telemedicine platforms commissioned on a nationwide scale. First pioneered with primitive 35-mm slide film retinal photography, the last decade has seen an emergence of high resolution and widefield imaging devices, which may reveal extents of DR indiscernible to the clinician but with implications of potential earlier identification. Similar progress has been seen in image analysis approaches - automated image analysis of retinal photographs of DR has evolved from qualitative feature detection to rules-based algorithms to autonomous artificial intelligence-powered classification. Such models have, relatively rapidly, been validated and are now receiving approval from health regulation authorities with deployment into the clinical sphere. In this review, we chart the evolution of global DR screening programmes since their inception highlighting major milestones in healthcare infrastructure, telemedicine approaches and imaging devices that have shaped the robust and effective frameworks recognised today. We also provide an outlook for the future of DR screening in the context of recent technological advancements with respect to their limitations in current times.

The American Society of Retina Specialists Artificial Intelligence Task Force Report

Artificial intelligence (AI) is a growing area that relies on the heavy use of diagnostic imaging within the field of retina to offer exciting advancements in diagnostic capability to better understand and manage retinal conditions such as diabetic retinopathy, diabetic macular edema, age-related macular degeneration, and retinopathy of prematurity. However, there are discrepancies between the findings of these AI programs and their referral recommendations compared with evidence-based referral patterns, such as Preferred Practice Patterns by the American Academy of Ophthalmology. The overall focus of this task force report is to first describe the work in AI being completed in the management of retinal conditions. This report also discusses the guidelines of the Preferred Practice Pattern and how they can be used in the emerging field of AI.

Diving Deep into Deep Learning: An Update on Artificial Intelligence in Retina

Purpose of Review

In the present article, we will provide an understanding and review of artificial intelligence in the subspecialty of retina and its potential applications within the specialty.

Recent Findings

Given the significant use of diagnostic imaging within retina, this subspecialty is a fitting area for the incorporation of artificial intelligence. Researchers have aimed at creating models to assist in the diagnosis and management of retinal disease as well as in the prediction of disease course and treatment response. Most of this work thus far has focused on diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity, although other retinal diseases have started to be explored as well.

Summary

Artificial intelligence is well-suited to transform the practice of ophthalmology. A basic understanding of the technology is important for its effective implementation and growth.

Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software

Purposes: To describe the development and validation of an artificial intelligence-based, deep learning algorithm (DeepDR) for the detection of diabetic retinopathy (DR) in retinal fundus photographs. Methods: Five hundred fundus images, which had detailed labelling of DR lesions, were transmitted to be analysed, including localization of the optic disk and macular, vessel segmentation, detection of lesions, and grading of DR. The multi-level iterative method of convolutional neural network and the strategy of enhanced learning were used to improve the accuracy of the system (DeepDR) for grading DR. Three public data sets were used to further train the software. The final grading results were tested based on the fundus images provided by the hospitals. Results: For 6788 fundus images (both macular and disc centred) of two Hospital Eye Center, the detection of microaneurysm, haemorrhage and hard exudates had an accuracy of 99.7%, 98.4% and 98.1%, respectively. The current algorithm accuracy was 0.96. Another 20,000 fundus images from community screening were selected, and 7593 photos of poor quality were excluded according to quality standards. Accuracy for accurate staging of fundus photos: accuracy was 0.9179. The sensitivity, specificity and area under the curve (AUC) were 80.58%, 95.77% and 0.9327, respectively. Conclusions: This artificial intelligence-based DeepDR can be used with high accuracy for the detection of DR in retinal images. This technology offers the potential to increase the efficiency and accessibility of DR screening programs.

Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening

PURPOSE

To develop a deep learning image assessment software VeriSee™ and to validate its accuracy in grading the severity of diabetic retinopathy (DR).

METHODS

Diabetic patients who underwent single-field, nonmydriatic, 45-degree color retinal fundus photography at National Taiwan University Hospital between July 2007 and June 2017 were retrospectively recruited. A total of 7524 judgeable color fundus images were collected and were graded for the severity of DR by ophthalmologists. Among these pictures, 5649 along with another 31,612 color fundus images from the EyePACS dataset were used for model training of VeriSee™. The other 1875 images were used for validation and were graded for the severity of DR by VeriSee™, ophthalmologists, and internal physicians. Area under the receiver operating characteristic curve (AUC) for VeriSee™, and the sensitivities and specificities for VeriSee™, ophthalmologists, and internal physicians in diagnosing DR were calculated.

RESULTS

The AUCs for VeriSee™ in diagnosing any DR, referable DR and proliferative diabetic retinopathy (PDR) were 0.955, 0.955 and 0.984, respectively. VeriSee™ had better sensitivities in diagnosing any DR and PDR (92.2% and 90.9%, respectively) than internal physicians (64.3% and 20.6%, respectively) (P < 0.001 for both). VeriSee™ also had better sensitivities in diagnosing any DR and referable DR (92.2% and 89.2%, respectively) than ophthalmologists (86.9% and 71.1%, respectively) (P < 0.001 for both), while ophthalmologists had better specificities.

CONCLUSION

VeriSee™ had good sensitivity and specificity in grading the severity of DR from color fundus images. It may offer clinical assistance to non-ophthalmologists in DR screening with nonmydriatic retinal fundus photography.

Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review

BACKGROUND

Effective screening is a desirable method for the early detection and successful treatment for diabetic retinopathy, and fundus photography is currently the dominant medium for retinal imaging due to its convenience and accessibility. Manual screening using fundus photographs has however involved considerable costs for patients, clinicians and national health systems, which has limited its application particularly in less-developed countries. The advent of artificial intelligence, and in particular deep learning techniques, has however raised the possibility of widespread automated screening.

MAIN TEXT

In this review, we first briefly survey major published advances in retinal analysis using artificial intelligence. We take care to separately describe standard multiple-field fundus photography, and the newer modalities of ultra-wide field photography and smartphone-based photography. Finally, we consider several machine learning concepts that have been particularly relevant to the domain and illustrate their usage with extant works.

CONCLUSIONS

In the ophthalmology field, it was demonstrated that deep learning tools for diabetic retinopathy show clinically acceptable diagnostic performance when using colour retinal fundus images. Artificial intelligence models are among the most promising solutions to tackle the burden of diabetic retinopathy management in a comprehensive manner. However, future research is crucial to assess the potential clinical deployment, evaluate the cost-effectiveness of different DL systems in clinical practice and improve clinical acceptance.

Screening for diabetic retinopathy: new perspectives and challenges

Although the prevalence of all stages of diabetic retinopathy has been declining since 1980 in populations with improved diabetes control, the crude prevalence of visual impairment and blindness caused by diabetic retinopathy worldwide increased between 1990 and 2015, largely because of the increasing prevalence of type 2 diabetes, particularly in low-income and middle-income countries. Screening for diabetic retinopathy is essential to detect referable cases that need timely full ophthalmic examination and treatment to avoid permanent visual loss. In the past few years, personalised screening intervals that take into account several risk factors have been proposed, with good cost-effectiveness ratios. However, resources for nationwide screening programmes are scarce in many countries. New technologies, such as scanning confocal ophthalmology with ultrawide field imaging and handheld mobile devices, teleophthalmology for remote grading, and artificial intelligence for automated detection and classification of diabetic retinopathy, are changing screening strategies and improving cost-effectiveness. Additionally, emerging evidence suggests that retinal imaging could be useful for identifying individuals at risk of cardiovascular disease or cognitive impairment, which could expand the role of diabetic retinopathy screening beyond the prevention of sight-threatening disease.

Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard

PURPOSE

To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.

DESIGN

Retrospective, cross-sectional, longitudinal cohort study.

PARTICIPANTS

Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models.

METHOD

We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma.

MAIN OUTCOME MEASURES

The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma.

RESULTS

After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%.

CONCLUSIONS

The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression.

Overview of Therapeutic Inertia in Diabetes: Prevalence, Causes, and Consequences

Many people with diabetes do not achieve individualized treatment targets. Therapeutic inertia, the underuse of effective therapies in preventing serious clinical end points, is a frequent, important contributor to this failure. Clinicians, patients, health systems, payors, and producers of medications, devices, and other products for those with diabetes all play a role in the development of therapeutic inertia and can all help to reduce it.

Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning

PURPOSE

To assess the use of deep learning (DL) for computer-assisted glaucoma identification, and the impact of training using images selected by an active learning strategy, which minimizes labelling cost. Additionally, this study focuses on the explainability of the glaucoma classifier.

METHODS

This original investigation pooled 8433 retrospectively collected and anonymized colour optic disc-centred fundus images, in order to develop a deep learning-based classifier for glaucoma diagnosis. The labels of the various deep learning models were compared with the clinical assessment by glaucoma experts. Data were analysed between March and October 2018. Sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and amount of data used for discriminating between glaucomatous and non-glaucomatous fundus images, on both image and patient level.

RESULTS

Trained using 2072 colour fundus images, representing 42% of the original training data, the trained DL model achieved an AUC of 0.995, sensitivity and specificity of, respectively, 98.0% (CI 95.5%-99.4%) and 91% (CI 84.0%-96.0%), for glaucoma versus non-glaucoma patient referral.

CONCLUSIONS

These results demonstrate the benefits of deep learning for automated glaucoma detection based on optic disc-centred fundus images. The combined use of transfer and active learning in the medical community can optimize performance of DL models, while minimizing the labelling cost of domain-specific mavens. Glaucoma experts are able to make use of heat maps generated by the deep learning classifier to assess its decision, which seems to be related to inferior and superior neuroretinal rim (within ONH), and RNFL in superotemporal and inferotemporal zones (outside ONH).

Diabetic retinopathy screening using a virtual reading center

AIMS

To summarize the effects of centralization of diabetic fundus photograph interpretation into a virtual reading center.

METHODS

In 2016 Kaiser Permanente Northern California, a large, membership-based health plan with an ethnically and racially diverse population, centralized diabetic retinopathy screening into a virtual reading center. Retina screens were based on single field, 45-degree fundus photographs. We compared the accuracy of photography interpretation the year before centralization to the year after using masked reads performed by retina specialists of 1000 randomly selected screens from each time period.

RESULTS

In all, 1902 patient screens with adequate quality images were included in the primary analysis. Images from pre-centralization screens were largely read by ophthalmologists (76.2%), while screens post-centralization were mainly read by optometrists (84.6%). Despite being interpreted by readers with lower levels of professional training, the sensitivity of screening increased from 43.9% (95% CI 38.0-49.8%) to 66.0% (95% CI 60.5-71.4%).

CONCLUSION

A move to a centralized virtual reading center was associated with improved accuracy of diabetic retinopathy screening.

Artificial Intelligence in Medicine: Where Are We Now?

Artificial intelligence in medicine has made dramatic progress in recent years. However, much of this progress is seemingly scattered, lacking a cohesive structure for the discerning observer. In this article, we will provide an up-to-date review of artificial intelligence in medicine, with a specific focus on its application to radiology, pathology, ophthalmology, and dermatology. We will discuss a range of selected papers that illustrate the potential uses of artificial intelligence in a technologically advanced future.

Artificial intelligence in clinical and genomic diagnostics

Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.

The virtual physical exam in the 21st century

The modern Western medical encounter follows a strict framework that weaves subjective and objective components into a unifying diagnosis. As health care changes to incorporate new technology, such as virtual health care, the components that lead to diagnosis must likewise evolve. The virtual physical exam has limitations compared with the traditional exam. Despite this limitation, every year more patients are seen virtually with high satisfaction. Data have shown that supplementary real-time patient-provider video telemedicine increases access and extends established patient-physician relationships which will likely fuel increased telemedicine adoption even further. However, to date, there are limited data regarding the validity of the virtual examination compared with the traditional physical exam. In this paper, we review the use of developing technology related to the virtual physical exam.

Validation of a Deep Learning Algorithm for Diabetic Retinopathy

Background: To validate our deep learning algorithm (DLA) to read diabetic retinopathy (DR) retinographies. Introduction: Currently DR detection is made by retinography; due to its increasing diabetes mellitus incidence we need to find systems that help us to screen DR. Materials and Methods: The DLA was built and trained using 88,702 images from EyePACS, 1,748 from Messidor-2, and 19,230 from our own population. For validation a total of 38,339 retinographies from 17,669 patients (obtained from our DR screening databases) were read by a DLA and compared by four senior retina ophthalmologists for detecting any-DR and referable-DR. We determined the values of Cohen's weighted Kappa (CWK) index, sensitivity (S), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV), and errors type I and II. Results: The results of the DLA to detect any-DR were: CWK = 0.886 ± 0.004 (95% confidence interval [CI] 0.879-0.894), S = 0.967%, SP = 0.976%, PPV = 0.836%, and NPV = 0.996%. The error type I = 0.024, and the error type II = 0.004. Likewise, the referable-DR results were: CWK = 0.809 (95% CI 0.798-0.819), S = 0.998, SP = 0.968, PPV = 0.701, NPV = 0.928, error type I = 0.032, and error type II = 0.001. Discussion: Our DLA can be used as a high confidence diagnostic tool to help in DR screening, especially when it might be difficult for ophthalmologists or other professionals to identify. It can identify patients with any-DR and those that should be referred. Conclusions: The DLA can be valid to aid in screening of DR.

The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes

Background: Current manual diabetic retinopathy (DR) screening using eye care experts cannot scale to screen the growing population of diabetes patients who are at risk for vision loss. EyeArt system is an automated, cloud-based artificial intelligence (AI) eye screening technology designed to easily detect referral-warranted DR immediately through automated analysis of patient's retinal images. Methods: This retrospective study assessed the diagnostic efficacy of the EyeArt system v2.0 analyzing 850,908 fundus images from 101,710 consecutive patient visits, collected from 404 primary care clinics. Presence or absence of referral-warranted DR (more than mild nonproliferative DR [NPDR]) was automatically detected by the EyeArt system for each patient encounter, and its performance was compared against a clinical reference standard of quality-assured grading by rigorously trained certified ophthalmologists and optometrists. Results: Of the 101,710 visits, 75.7% were nonreferable, 19.3% were referable to an eye care specialist, and in 5.0%, the DR level was unknown as per the clinical reference standard. EyeArt screening had 91.3% (95% confidence interval [CI]: 90.9-91.7) sensitivity and 91.1% (95% CI: 90.9-91.3) specificity. For 5446 encounters with potentially treatable DR (more than moderate NPDR and/or diabetic macular edema), the system provided a positive "refer" output to 5363 encounters achieving sensitivity of 98.5%. Conclusions: This study captures variations in real-world clinical practice and shows that an AI DR screening system can be safe and effective in the real world. This study demonstrates the value of this easy-to-use, automated tool for endocrinologists, diabetologists, and general practitioners to address the growing need for DR screening and monitoring.

Artificial intelligence for diabetic retinopathy screening: a review

Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing population, this poses significant challenge to perform diabetic retinopathy (DR) screening for these patients. Artificial intelligence (AI) using machine learning and deep learning have been adopted by various groups to develop automated DR detection algorithms. This article aims to describe the state-of-art AI DR screening technologies that have been described in the literature, some of which are already commercially available. All these technologies were designed using different training datasets and technical methodologies. Although many groups have published robust diagnostic performance of the AI algorithms for DR screening, future research is required to address several challenges, for examples medicolegal implications, ethics, and clinical deployment model in order to expedite the translation of these novel technologies into the healthcare setting.

Universal artificial intelligence platform for collaborative management of cataracts

Purpose

To establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.

Methods

The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services.

Results

The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be ‘referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern.

Conclusions

The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

Diabetes Care Editors' Expert Forum 2018: Managing Big Data for Diabetes Research and Care

Technological progress in the past half century has greatly increased our ability to collect, store, and transmit vast quantities of information, giving rise to the term "big data." This term refers to very large data sets that can be analyzed to identify patterns, trends, and associations. In medicine-including diabetes care and research-big data come from three main sources: electronic medical records (EMRs), surveys and registries, and randomized controlled trials (RCTs). These systems have evolved in different ways, each with strengths and limitations. EMRs continuously accumulate information about patients and make it readily accessible but are limited by missing data or data that are not quality assured. Because EMRs vary in structure and management, comparisons of data between health systems may be difficult. Registries and surveys provide data that are consistently collected and representative of broad populations but are limited in scope and may be updated only intermittently. RCT databases excel in the specificity, completeness, and accuracy of their data, but rarely include a fully representative sample of the general population. Also, they are costly to build and seldom maintained after a trial's end. To consider these issues, and the challenges and opportunities they present, the editors of Diabetes Care convened a group of experts in management of diabetes-related data on 21 June 2018, in conjunction with the American Diabetes Association's 78th Scientific Sessions in Orlando, FL. This article summarizes the discussion and conclusions of that forum, offering a vision of benefits that might be realized from prospectively designed and unified data-management systems to support the collective needs of clinical, surveillance, and research activities related to diabetes.

Update on Screening for Sight-Threatening Diabetic Retinopathy

PURPOSE

The aim of this article was to describe recent advances in the use of new technology in diabetic retinopathy screening by looking at studies that assessed the effectiveness and cost-effectiveness of these technologies.

METHODS

The author conducts an ongoing search for articles relating to screening or management of diabetic retinopathy utilising Zetoc with keywords and contents page lists from relevant journals.

RESULTS

The areas discussed in this article are reference standards, alternatives to digital photography, area of retina covered by the screening method, size of the device and hand-held cameras, mydriasis versus non-mydriasis or a combination, measurement of distance visual acuity, grading of images, use of automated grading analysis and cost-effectiveness of the new technologies.

CONCLUSIONS

There have been many recent advances in technology that may be adopted in the future by screening programmes for sight-threatening diabetic retinopathy but each device will need to demonstrate effectiveness and cost-effectiveness before more widespread adoption.

Automated OCT angiography image quality assessment using a deep learning algorithm

PURPOSE

To expedite and to standardize the process of image quality assessment in optical coherence tomography angiography (OCTA) using a specialized deep learning algorithm (DLA).

METHODS

Two hundred randomly chosen en-face macular OCTA images of the central 3 × 3 mm² superficial vascular plexus were evaluated retrospectively by an OCTA experienced reader. Images were defined either as sufficient (group 1, n = 100) or insufficient image quality (group 2, n = 100) based on Motion Artifact Score (MAS) and Segmentation Accuracy Score (SAS). Subsequently, a pre-trained multi-layer deep convolutional neural network (DCNN) was trained and validated with 160 of these en-face OCTA scans (group 1: 80; group 2: 80). Training accuracy, validation accuracy, and cross-entropy were computed. The DLA was tested in detecting 40 untrained OCTA images (group 1: 20; group 2: 20). An insufficient image quality probability score (IPS) and a sufficient image quality probability score (SPS) were calculated.

RESULTS

Training accuracy was 97%, validation accuracy 100%, and cross entropy 0.12. A total of 90% (18/20) of the OCTA images with insufficient image quality and 90% (18/20) with sufficient image quality were correctly classified by the DLA. Mean IPS was 0.88 ± 0.21, and mean SPS was 0.84 ± 0.19. Discrimination between both groups was highly significant (p < 0.001). Sensitivity of the DLA was 90.0%, specificity 90.0%, and accuracy 90.0%. Coefficients of variation were 0.96 ± 1.9% (insufficient quality) and 1.14 ± 1.6% (sufficient quality).

CONCLUSIONS

Deep learning (DL) appears to be a potential approach to automatically distinguish between sufficient and insufficient OCTA image quality. DL may contribute to establish image quality standards in this recent imaging modality.

The unmet need for better risk stratification of non-proliferative diabetic retinopathy

Diabetic retinopathy is a common microvascular complication of diabetes and remains one of the leading causes of preventable blindness in working-age people. Non-proliferative diabetic retinopathy is the earliest stage of diabetic retinopathy and is typically asymptomatic. Currently, the severity of diabetic retinopathy is assessed using semi-quantitative grading systems based on the presence or absence of retinal lesions. These methods are well validated, but do not predict those at high risk of rapid progression to sight-threatening diabetic retinopathy; therefore, new approaches for identifying these people are a current unmet need. We evaluated published data reporting the lesion characteristics associated with different progression profiles in people with non-proliferative diabetic retinopathy. Based on these findings, we propose that additional assessments of features of non-proliferative diabetic retinopathy lesions may help to stratify people based on the likelihood of rapid progression. In addition to the current classification, the following measurements should be considered: the shape and size of lesions; whether lesions are angiogenic in origin; the location of lesions, including predominantly peripheral lesions; and lesion turnover and dynamics. For lesions commonly seen in hypertensive retinopathy, a detailed assessment of potential concomitant diseases is also recommended. We believe that natural history studies of these changes will help characterize these non-proliferative diabetic retinopathy progression profiles and advance our understanding of the pathogenesis of diabetic retinopathy in order to individualize management of people with diabetic retinopathy.

Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting

OBJECTIVE

To determine the diagnostic accuracy in a real-world primary care setting of a deep learning-enhanced device for automated detection of diabetic retinopathy (DR).

RESEARCH DESIGN AND METHODS

Retinal images of people with type 2 diabetes visiting a primary care screening program were graded by a hybrid deep learning-enhanced device (IDx-DR-EU-2.1; IDx, Amsterdam, the Netherlands), and its classification of retinopathy (vision-threatening [vt]DR, more than mild [mtm]DR, and mild or more [mom]DR) was compared with a reference standard. This reference standard consisted of grading according to the International Clinical Classification of DR by the Rotterdam Study reading center. We determined the diagnostic accuracy of the hybrid deep learning-enhanced device (IDx-DR-EU-2.1) against the reference standard.

RESULTS

A total of 1,616 people with type 2 diabetes were imaged. The hybrid deep learning-enhanced device's sensitivity/specificity against the reference standard was, respectively, for vtDR 100% (95% CI 77.1-100)/97.8% (95% CI 96.8-98.5) and for mtmDR 79.4% (95% CI 66.5-87.9)/93.8% (95% CI 92.1-94.9).

CONCLUSIONS

The hybrid deep learning-enhanced device had high diagnostic accuracy for the detection of both vtDR (although the number of vtDR cases was low) and mtmDR in a primary care setting against an independent reading center. This allows its' safe use in a primary care setting.

Methods to label, image, and analyze the complex structural architectures of microvascular networks

Microvascular networks play key roles in oxygen transport and nutrient delivery to meet the varied and dynamic metabolic needs of different tissues throughout the body, and their spatial architectures of interconnected blood vessel segments are highly complex. Moreover, functional adaptations of the microcirculation enabled by structural adaptations in microvascular network architecture are required for development, wound healing, and often invoked in disease conditions, including the top eight causes of death in the Unites States. Effective characterization of microvascular network architectures is not only limited by the available techniques to visualize microvessels but also reliant on the available quantitative metrics that accurately delineate between spatial patterns in altered networks. In this review, we survey models used for studying the microvasculature, methods to label and image microvessels, and the metrics and software packages used to quantify microvascular networks. These programs have provided researchers with invaluable tools, yet we estimate that they have collectively attained low adoption rates, possibly due to limitations with basic validation, segmentation performance, and nonstandard sets of quantification metrics. To address these existing constraints, we discuss opportunities to improve effectiveness, rigor, and reproducibility of microvascular network quantification to better serve the current and future needs of microvascular research.

Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review

Rising prevalence of diabetes worldwide has necessitated the implementation of population-based diabetic retinopathy (DR) screening programs that can perform retinal imaging and interpretation for extremely large patient cohorts in a rapid and sensitive manner while minimizing inappropriate referrals to retina specialists. While most current screening programs employ mydriatic or nonmydriatic color fundus photography and trained image graders to identify referable DR, new imaging modalities offer significant improvements in diagnostic accuracy, throughput, and affordability. Smartphone-based fundus photography, macular optical coherence tomography, ultrawide-field imaging, and artificial intelligence-based image reading address limitations of current approaches and will likely become necessary as DR becomes more prevalent. Here we review current trends in imaging for DR screening and emerging technologies that show potential for improving upon current screening approaches.