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

A Multicenter Clinical Study of the Automated Fundus Screening Algorithm

Purpose

To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts.

Results

There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918-0.967), 0.891 (95% CI, 0.855-0.919), and 0.901 (95% CI-0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915-0.965), 0.993 (95% CI-0.986, 0.996), and 0.955 (95% CI-0.939, 0.968), respectively.

Methods

We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD.

Conclusions

Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras.

Translational Relevance

These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.

Automatic Screening of the Eyes in a Deep-Learning–Based Ensemble Model Using Actual Eye Checkup Optical Coherence Tomography Images

Eye checkups have become increasingly important to maintain good vision and quality of life. As the population requiring eye checkups increases, so does the clinical work burden of clinicians. An automatic screening algorithm to reduce the clinicians’ workload is necessary. Machine learning (ML) has recently become one of the chief techniques for automated image recognition and is a helpful tool for identifying ocular diseases. However, the accuracy of ML models is lower in a clinical setting than in the laboratory. The performance of ML models depends on the training dataset. Eye checkups often prioritize speed and minimize image processing. Data distribution differs from the training dataset and, consequently, decreases prediction performance. The study aim was to investigate an ML model to screen for retinal diseases from low-quality optical coherence tomography (OCT) images captured during actual eye chechups to prevent a dataset shift. The ensemble model with convolutional neural networks (CNNs) and random forest models showed high screening performance in the single-shot OCT images captured during the actual eye checkups. Our study indicates the strong potential of the ensemble model combining the CNN and random forest models in accurately predicting abnormalities during eye checkups.

Automated image curation in diabetic retinopathy screening using deep learning

Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually. We developed and validated single and multi-output laterality, retinal presence, retinal field and gradability classification deep learning (DL) models for automated curation. The internal dataset comprised of 7743 images from DR screening (UK) with 1479 external test images (Portugal and Paraguay). Internal vs external multi-output laterality AUROC were right (0.994 vs 0.905), left (0.994 vs 0.911) and unidentifiable (0.996 vs 0.680). Retinal presence AUROC were (1.000 vs 1.000). Retinal field AUROC were macula (0.994 vs 0.955), nasal (0.995 vs 0.962) and other retinal field (0.997 vs 0.944). Gradability AUROC were (0.985 vs 0.918). DL effectively detects laterality, retinal presence, retinal field and gradability of DR screening images with generalisation between centres and populations. DL models could be used for automated image curation within DR screening.

Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress' prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence?

DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge

We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.

Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets

Automated glaucoma detection using deep learning may increase the diagnostic rate of glaucoma to prevent blindness, but generalizable models are currently unavailable despite the use of huge training datasets. This study aims to evaluate the performance of a convolutional neural network (CNN) classifier trained with a limited number of high-quality fundus images in detecting glaucoma and methods to improve its performance across different datasets. A CNN classifier was constructed using EfficientNet B3 and 944 images collected from one medical center (core model) and externally validated using three datasets. The performance of the core model was compared with (1) the integrated model constructed by using all training images from the four datasets and (2) the dataset-specific model built by fine-tuning the core model with training images from the external datasets. The diagnostic accuracy of the core model was 95.62% but dropped to ranges of 52.5–80.0% on the external datasets. Dataset-specific models exhibited superior diagnostic performance on the external datasets compared to other models, with a diagnostic accuracy of 87.50–92.5%. The findings suggest that dataset-specific tuning of the core CNN classifier effectively improves its applicability across different datasets when increasing training images fails to achieve generalization.

Variability of Grading DR Screening Images among Non-Trained Retina Specialists

Poland has never had a widespread diabetic retinopathy (DR) screening program and subsequently has no purpose-trained graders and no established grader training scheme. Herein, we compare the performance and variability of three retinal specialists with no additional DR grading training in assessing images from 335 real-life screening encounters and contrast their performance against IDx-DR, a US Food and Drug Administration (FDA) approved DR screening suite. A total of 1501 fundus images from 670 eyes were assessed by each grader with a final grade on a per-eye level. Unanimous agreement between all graders was achieved for 385 eyes, and 110 patients, out of which 98% had a final grade of no DR. Thirty-six patients had final grades higher than mild DR, out of which only two had no grader disagreements regarding severity. A total of 28 eyes underwent adjudication due to complete grader disagreement. Four patients had discordant grades ranging from no DR to severe DR between the human graders and IDx-DR. Retina specialists achieved kappa scores of 0.52, 0.78, and 0.61. Retina specialists had relatively high grader variability and only a modest concordance with IDx-DR results. Focused training and verification are recommended for any potential DR graders before assessing DR screening images.

Changes and significance of retinal blood oxygen saturation and oxidative stress indexes in patients with diabetic retinopathy

BACKGROUND

Diabetic retinopathy (DR) is a diabetic complication that can severely affect the patients’ vision, eventually leading to blindness. DR is the most important manifestation of diabetic micro-vasculopathy and is mainly related to the course of diabetes and the degree of blood glucose control, while the age of diabetes onset, sex, and type of diabetes have little influence on it.

AIM

To explore the changes in blood oxygen saturation and oxidative stress indices of retinal vessels in patients with DR.

METHODS

In total, 94 patients (94 eyes) with DR (DR group) diagnosed at Jianyang people’s Hospital between March 2019 and June 2020, and 100 volunteers (100 eyes) (control group) without eye diseases, were included in this study. Arterial and venous blood oxygen saturation, retinal arteriovenous vessel diameter, and serum oxidative stress indicators in the two groups were compared. Based on the stage of the disease, the DR group was divided into the simple DR and proliferative DR groups for stratified analysis.

RESULTS

The oxygen saturation of the retinal vessels in the DR group was significantly higher than that in the control group (P < 0.05). The retinal vessel diameters between the DR and control groups were not significantly different. The serum malondialdehyde (MDA) and 8-hydroxydehydroguanosine (8-OHdG) levels in the DR group were significantly higher than those in the control group (P < 0.05). The serum superoxide dismutase (SOD) and reduced glutathione (GSH) levels in the DR group were significantly lower than those in the control group (P < 0.05). The oxygen saturation of the retinal vessels in the patients with proliferative DR was significantly higher than that in the patients with simple DR (P < 0.05). The retinal vessel diameter in patients with proliferative DR was not significantly different from that of patients with simple DR (P > 0.05). Serum MDA and 8-OHdG levels in patients with proliferative DR were significantly higher than those in patients with simple DR (P < 0.05). Serum SOD and GSH levels in patients with proliferative DR were significantly lower than those in patients with simple DR (P < 0.05).

CONCLUSION

Increased blood oxygen saturation of retinal arteries and veins and increased oxidative stress damage in patients with DR may be associated with decreased retinal capillary permeability and arterial oxygen dispersion, possibly reflecting the patient’s condition.

Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening

PURPOSE

Artificial intelligence (AI) technology is poised to revolutionize modern delivery of health care services. We set to evaluate the patient perspective of AI use in diabetic retinal screening.

DESIGN

Survey.

METHODS

Four hundred thirty-eight patients undergoing diabetic retinal screening across New Zealand participated in a survey about their opinion of AI technology in retinal screening. The survey consisted of 13 questions covering topics of awareness, trust, and receptivity toward AI systems.

RESULTS

The mean age was 59 years. The majority of participants identified as New Zealand European (50%), followed by Asian (31%), Pacific Islander (10%), and Maori (5%). Whilst 73% of participants were aware of AI, only 58% have heard of it being implemented in health care. Overall, 78% of respondents were comfortable with AI use in their care, with 53% saying they would trust an AI-assisted screening program as much as a health professional. Despite having a higher awareness of AI, younger participants had lower trust in AI systems. A higher proportion of Maori and Pacific participants indicated a preference toward human-led screening. The main perceived benefits of AI included faster diagnostic speeds and greater accuracy.

CONCLUSIONS

There is low awareness of clinical AI applications among our participants. Despite this, most are receptive toward the implementation of AI in diabetic eye screening. Overall, there was a strong preference toward continual involvement of clinicians in the screening process. There are key recommendations to enhance the receptivity of the public toward incorporation of AI into retinal screening programs.

Evaluation of an Artificial Intelligence System for the Detection of Diabetic Retinopathy in Chinese Community Healthcare Centers

Objective

To evaluate the sensitivity and specificity of a Comprehensive Artificial Intelligence Retinal Expert (CARE) system for detecting diabetic retinopathy (DR) in a Chinese community population.

Methods

This was a cross-sectional, diagnostic study. Participants with a previous diagnosis of diabetes from three Chinese community healthcare centers were enrolled in the study. Single-field color fundus photography was obtained and analyzed by the AI system and two ophthalmologists. Primary outcome measures included the sensitivity, specificity, positive predictive value, and negative predictive value with their 95% confidence intervals (CIs) of the AI system in detecting DR and diabetic macular edema (DME).

Results

In this study, 443 subjects (848 eyes) were enrolled, and 283 (63.88%) were men. The mean age was 52.09 (11.51) years (range 18–82 years); 266 eyes were diagnosed with any DR, 233 with more-than-mild diabetic retinopathy (mtmDR), 112 with vision-threatening diabetic retinopathy (vtDR), and 57 with DME. The image ability of the AI system was as high as 99.06%, whereas its sensitivity and specificity varied significantly in detecting DR with different severities. The sensitivity/specificity to detect any DR was 75.19% (95%CI 69.47–80.17)/93.99% (95%CI 91.65–95.71), mtmDR 78.97% (95%CI 73.06–83.90)/92.52% (95%CI 90.07–94.41), vtDR 33.93% (95%CI 25.41–43.56)/97.69% (95%CI 96.25–98.61), and DME 47.37% (95%CI 34.18–60.91)/93.99% (95%CI 91.65–95.71).

Conclusions

This multicenter cross-sectional diagnostic study noted the safety and reliability of the CARE system for DR (especially mtmDR) detection in Chinese community healthcare centers. The system may effectively solve the dilemma faced by Chinese community healthcare centers: due to the lack of ophthalmic expertise of primary physicians, DR diagnosis and referral are not timely.

Evaluation of a computer-based facial dysmorphology analysis algorithm (Face2Gene) using standardized textbook photos

BACKGROUND

Genetic syndromes often have ocular involvement. Ophthalmologists may have difficulty identifying dysmorphic features in genetic syndrome evaluations. We investigated the sensitivity and specificity of Face2Gene (F2G), a digital image analysis software trained on integrating dysmorphic features, by analysing patient photos from genetics textbooks.

METHODS

We analysed all clear facial photos contained within the textbooks Smith's Recognizable Patterns of Human Malformation and Genetic Diseases of the Eye using F2G under standard lighting conditions. Variables captured include colour versus grey scale photo, the gender of the patient (if known), age of the patient (if known), disease categories, diagnosis as listed in the textbook, and whether the disease has ophthalmic involvement (as described in the textbook entries). Any photos rejected by F2G were excluded. We analysed the data for accuracy, sensitivity, and specificity based on disease categories as outlined in Smith's Recognizable Patterns of Malformation.

RESULTS

We analysed 353 photos found within two textbooks. The exact book diagnosis was identified by F2G in 150 (42.5%) entries, and was included in the top three differential diagnoses in 191 (54.1%) entries. F2G is highly sensitive for craniosynostosis syndromes (point estimate [PE] 80.0%, 95% confidence interval [CI] 56.3-94.3%, P = 0.0118) and syndromes with facial defects as a major feature (PE 77.8%, 95% CI 52.4-93.6%, P = 0.0309). F2G was highly specific (PE > 83percentage with P < 0.001) for all disease categories.

CONCLUSIONS

F2G is a useful tool for paediatric ophthalmologists to help build a differential diagnosis when evaluating children with dysmorphic facial features.

Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images

INTRODUCTION

Comparison of diabetic retinopathy (DR) severity between autonomous Artificial Intelligence (AI)-based outputs from an FDA-approved screening system and human retina specialists' gradings from ultra-widefield (UWF) colour images.

METHODS

Asymptomatic diabetics without a previous diagnosis of DR were included in this prospective observational pilot study. Patients were imaged with autonomous AI (IDx-DR, Digital Diagnostics). For each eye, two 45° colour fundus images were analysed by a secure server-based AI algorithm. UWF colour fundus imaging was performed using Optomap (Daytona, Optos). The International Clinical DR severity score was assessed both on a 7-field area projection (7F-mask) according to the early treatment diabetic retinopathy study (ETDRS) and on the total gradable area (UWF full-field) up to the far periphery on UWF images.

RESULTS

Of 54 patients included (n = 107 eyes), 32 were type 2 diabetics (11 females). Mean BCVA was 0.99 ± 0.25. Autonomous AI diagnosed 16 patients as negative, 28 for moderate DR and 10 for having a vision-threatening disease (severe DR, proliferative DR, diabetic macular oedema). Based on the 7F-mask grading with the eye with the worse grading defining the DR stage 23 patients were negative for DR, 11 showed mild, 19 moderate and 1 severe DR. When UWF full-field was analysed, 20 patients were negative for DR, while the number of mild, moderate and severe DR patients were 12, 21, and 1, respectively.

CONCLUSIONS

The autonomous AI-based DR examination demonstrates sufficient accuracy in diagnosing asymptomatic non-proliferative diabetic patients with referable DR even compared to UWF imaging evaluated by human experts offering a suitable method for DR screening.

Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities

OBJECTIVES

Clinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health technology assessment methods.

METHODS

We used an existing broad value framework to assess potential ways AI can provide good value for money. We also developed a rubric of how economic evaluations of AI should vary depending on the case of its use.

RESULTS

We found that the measurement of core elements of value-health outcomes and cost-are complicated by AI because its generalizability across different populations is often unclear and because its use may necessitate reconfigured clinical processes. Clinicians' productivity may improve when AI is used. If poorly implemented though, AI may also cause clinicians' workload to increase. Some AI has been found to exacerbate health disparities. Nevertheless, AI may promote equity by expanding access to medical care and, when properly trained, providing unbiased diagnoses and prognoses. The approach to assessment of AI should vary based on its use case: AI that creates new clinical possibilities can improve outcomes, but regulation and evidence collection may be difficult; AI that extends clinical expertise can reduce disparities and lower costs but may result in overuse; and AI that automates clinicians' work can improve productivity but may reduce skills.

CONCLUSIONS

The potential uses of clinical AI create challenges for health technology assessment methods originally developed for pharmaceuticals and medical devices. Health economists should be prepared to examine data collection and methods used to train AI, as these may impact its future value.

Imaging diabetic retinal disease: clinical imaging requirements

Diabetic retinopathy (DR) is a sight-threatening complication of diabetes mellitus (DM) and it contributes substantially to the burden of disease globally. During the last decades, the development of multiple imaging modalities to evaluate DR, combined with emerging treatment possibilities, has led to the implementation of large-scale screening programmes resulting in improved prevention of vision loss. However, not all patients are able to participate in such programmes and not all are at equal risk of DR development and progression. In this review, we discuss the relevance of the currently available imaging modalities for the evaluation of DR: colour fundus photography (CFP), ultrawide-field photography (UWFP), fundus fluorescein angiography (FFA), optical coherence tomography (OCT), OCT angiography (OCTA) and functional testing. Furthermore, we suggest where a particular imaging technique of DR may aid the evaluation of the disease in different clinical settings. Combining information from various imaging modalities may enable the design of more personalized care including the initiation of treatment and understanding the progression of disease more adequately.

[Use of artificial intelligence in screening for diabetic retinopathy at a tertiary diabetes center]

BACKGROUND

In 2018, IDx-DR was approved as a method to determine the degree of diabetic retinopathy (DR) using artificial intelligence (AI) by the FDA.

METHODS

We integrated IDx-DR into the consultation at a diabetology focus clinic and report the agreement between IDx-DR and fundoscopy as well as IDx-DR and ophthalmological image assessment and the influence of different camera systems.

RESULTS

Adequate image quality in miosis was achieved more frequently with the Topcon camera (n = 456; NW400, Topcon Medical Systems, Oakland, NJ, USA) compared with the Zeiss camera (n = 47; Zeiss VISUCAM 500, Carl Zeiss Meditec AG, Jena, Germany). Overall, IDx-DR analysis in miosis was possible in approximately 60% of the patients. All patients in whom IDx-DR analysis in miosis was not possible could be assessed by fundoscopy with dilated pupils. Within the group of images that could be evaluated, there was agreement between IDx-DR and ophthalmic fundoscopy in approximately 55%, overestimation of severity by IDx-DR in approximately 40% and underestimation in approximately 4%. The sensitivity (specificity) for detecting severe retinopathy requiring treatment was 95.7% (89.1%) for cases with fundus images that could be evaluated and 65.2% (66.7%) when all cases were considered (including those without images in miosis which could be evaluated). The kappa coefficient of 0.334 (p < 0.001) shows sufficient agreement between IDx-DR and physician's image analysis based on the fundus photograph, considering all patients with IDx-DR analysis that could be evaluated. The comparison between IDx-DR and the physician's funduscopy under the same conditions shows a low agreement with a kappa value of 0.168 (p < 0.001).

CONCLUSION

The present study shows the possibilities and limitations of AI-assisted DR screening. A major limitation is that sufficient images cannot be obtained in miosis in approximately 40% of patients. When sufficient images were available the IDx-DR and ophthalmological diagnosis matched in more than 50% of cases. Underestimation of severity by IDx-DR occurred only rarely. For integration into an ophthalmologist's practice, this system seems suitable. Without access to an ophthalmologist the high rate of insufficient images in miosis represents an important limitation.

A clinical perspective on the expanding role of artificial intelligence in age-related macular degeneration

In recent years, there has been intense development of artificial intelligence (AI) techniques, which have the potential to improve the clinical management of age-related macular degeneration (AMD) and facilitate the prevention of irreversible vision loss from this condition. Such AI techniques could be used as clinical decision support tools to: (i) improve the detection of AMD by community eye health practitioners, (ii) enhance risk stratification to enable personalised monitoring strategies for those with the early stages of AMD, and (iii) enable early detection of signs indicative of possible choroidal neovascularisation allowing triaging of patients requiring urgent review. This review discusses the latest developments in AI techniques that show promise for these tasks, as well as how they may help in the management of patients being treated for choroidal neovascularisation and in accelerating the discovery of new treatments in AMD.

Artificial intelligence perspective in the future of endocrine diseases

In recent years, artificial intelligence (AI) shows promising results in the diagnosis, prediction, and management of diseases. The move from handwritten medical notes to electronic health records and a huge number of digital data commenced in the era of big data in medicine. AI can improve physician performance and help better clinical decision making which is called augmented intelligence. The methods applied in the research of AI and endocrinology include machine learning, artificial neural networks, and natural language processing. Current research in AI technology is making major efforts to improve decision support systems for patient use. One of the best-known applications of AI in endocrinology was seen in diabetes management, which includes prediction, diagnosis of diabetes complications (measuring microalbuminuria, retinopathy), and glycemic control. AI-related technologies are being found to assist in the diagnosis of other endocrine diseases such as thyroid cancer and osteoporosis. This review attempts to provide insight for the development of prospective for AI with a focus on endocrinology.

Cross-Camera External Validation for Artificial Intelligence Software in Diagnosis of Diabetic Retinopathy

AIMS

To investigate the applicability of deep learning image assessment software VeriSee DR to different color fundus cameras for the screening of diabetic retinopathy (DR).

METHODS

Color fundus images of diabetes patients taken with three different nonmydriatic fundus cameras, including 477 Topcon TRC-NW400, 459 Topcon TRC-NW8 series, and 471 Kowa nonmyd 8 series that were judged as "gradable" by one ophthalmologist were enrolled for validation. VeriSee DR was then used for the diagnosis of referable DR according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Gradability, sensitivity, and specificity were calculated for each camera model.

RESULTS

All images (100%) from the three camera models were gradable for VeriSee DR. The sensitivity for diagnosing referable DR in the TRC-NW400, TRC-NW8, and non-myd 8 series was 89.3%, 94.6%, and 95.7%, respectively, while the specificity was 94.2%, 90.4%, and 89.3%, respectively. Neither the sensitivity nor the specificity differed significantly between these camera models and the original camera model used for VeriSee DR development (p = 0.40, p = 0.065, respectively).

CONCLUSIONS

VeriSee DR was applicable to a variety of color fundus cameras with 100% agreement with ophthalmologists in terms of gradability and good sensitivity and specificity for the diagnosis of referable DR.

Artificial intelligence for diabetic retinopathy

ABSTRACT

Diabetic retinopathy (DR) is an important cause of blindness globally, and its prevalence is increasing. Early detection and intervention can help change the outcomes of the disease. The rapid development of artificial intelligence (AI) in recent years has led to new possibilities for the screening and diagnosis of DR. An AI-based diagnostic system for the detection of DR has significant advantages, such as high efficiency, high accuracy, and lower demand for human resources. At the same time, there are shortcomings, such as the lack of standards for development and evaluation and the limited scope of application. This article demonstrates the current applications of AI in the field of DR, existing problems, and possible future development directions.

Study on Horizon Scanning with a Focus on the Development of AI-Based Medical Products: Citation Network Analysis

Horizon scanning for innovative technologies that might be applied to medical products and requires new assessment approaches to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. The purpose of this study is to confirm that citation network analysis and text mining for bibliographic information analysis can be used for horizon scanning of the rapidly developing field of AI-based medical technologies and extract the latest research trend information from the field. We classified 119,553 publications obtained from SCI constructed with the keywords “conventional,” “machine-learning,” or “deep-learning" and grouped them into 36 clusters, which demonstrated the academic landscape of AI applications. We also confirmed that one or two close clusters included the key articles on AI-based medical image analysis, suggesting that clusters specific to the technology were appropriately formed. Significant research progress could be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster. Then we tracked recent research trends by re-analyzing “young” clusters based on the average publication year of the constituent papers of each cluster. The latest topics in AI-based medical technologies include electrocardiograms and electroencephalograms (ECG/EEG), human activity recognition, natural language processing of clinical records, and drug discovery. We could detect rapid increase in research activity of AI-based ECG/EEG a few years prior to the issuance of the draft guidance by US-FDA. Our study showed that a citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of rapidly developing AI-based medical technologies.

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study

Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pre-trained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios. Such models were designed to classify a thousand classes of natural images. There are fundamental differences between these models and those dealing with medical imaging tasks regarding learned features. Most medical imaging applications range from two to ten different classes, where we suspect that it would not be necessary to employ deeper learning models. This paper investigates such a hypothesis and develops an experimental study to examine the corresponding conclusions about this issue. The lightweight convolutional neural network (CNN) model and the pre-trained models have been evaluated using three different medical imaging datasets. We have trained the lightweight CNN model and the pre-trained models with two scenarios which are with a small number of images once and a large number of images once again. Surprisingly, it has been found that the lightweight model trained from scratch achieved a more competitive performance when compared to the pre-trained model. More importantly, the lightweight CNN model can be successfully trained and tested using basic computational tools and provide high-quality results, specifically when using medical imaging datasets.

Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database

Background: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. Methods: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). Results: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. Conclusions: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database.

Progress of artificial intelligence in diabetic retinopathy screening

Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide, and the limited availability of qualified ophthalmologists restricts its early diagnosis. For the past few years, artificial intelligence technology has developed rapidly and has been applied in DR screening. The upcoming technology provides support on DR screening and improves the identification of DR lesions with a high sensitivity and specificity. This review aims to summarize the progress on automatic detection and classification models for the diagnosis of DR.

AI-based monitoring of retinal fluid in disease activity and under therapy

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical examination. Artificial intelligence (AI) with its capability to objectively identify, localize and quantify fluid introduces fully automated tools into OCT imaging for personalized disease management. Deep learning performance has already proven superior to human experts, including physicians and certified readers, in terms of accuracy and speed. Reproducible measurement of retinal fluid relies on precise AI-based segmentation methods that assign a label to each OCT voxel denoting its fluid type such as intraretinal fluid (IRF) and subretinal fluid (SRF) or pigment epithelial detachment (PED) and its location within the central 1-, 3- and 6-mm macular area. Such reliable analysis is most relevant to reflect differences in pathophysiological mechanisms and impacts on retinal function, and the dynamics of fluid resolution during therapy with different regimens and substances. Yet, an in-depth understanding of the mode of action of supervised and unsupervised learning, the functionality of a convolutional neural net (CNN) and various network architectures is needed. Greater insight regarding adequate methods for performance, validation assessment, and device- and scanning-pattern-dependent variations is necessary to empower ophthalmologists to become qualified AI users. Fluid/function correlation can lead to a better definition of valid fluid variables relevant for optimal outcomes on an individual and a population level. AI-based fluid analysis opens the way for precision medicine in real-world practice of the leading retinal diseases of modern times.

Telemedical Diabetic Retinopathy Screening in a Primary Care Setting: Quality of Retinal Photographs and Accuracy of Automated Image Analysis

Background: Screening for diabetic eye disease (DED) and general diabetes care is often separate, which leads to delays and low adherence to DED screening recommendations. Thus, we assessed the feasibility, achieved image quality, and possible barriers of telemedical DED screening in a point-of-care general practice setting and the accuracy of an automated algorithm for detection of DED.Methods: Patients with diabetes were recruited at general practices. Retinal images were acquired using a non-mydriatic camera (CenterVue, Italy) by medical assistants. Images were quality assessed and double graded by two graders. All images were also graded automatically using a commercially available artificial intelligence (AI) algorithm (EyeArt version 2.1.0, Eyenuk Inc.).Results: A total of 75 patients (147 eyes; mean age 69 years, 96% type 2 diabetes) were included. Most of the patients (51; 68%) preferred DED screening at the general practice, but only twenty-four (32%) were willing to pay for this service. Images of 63 patients (84%) were determined to be evaluable, and DED was diagnosed in 6 patients (8.0%). The algorithm's positive/negative predictive values (95% confidence interval) were 0.80 (0.28-0.99)/1.00 (0.92-1.00) and 0.75 (0.19-0.99)/0.98 (0.88-1.00) for detection of any DED and referral-warranted DED, respectively.Overall, the number of referrals was 18 (24%) for manual telemedical assessment and 31 (41%) for the artificial intelligence (AI) algorithm, resulting in a relative increase of referrals by 72% when using AI.Conclusions: Our study shows that achieved overall image quality in a telemedical GP-based DED screening was sufficient and that it would be accepted by medical assistants and patients in most cases. However, good image quality and integration into existing workflow remain challenging. Based on these findings, a larger-scale implementation study is warranted.

Diabetic Retinopathy Screening and Registration in Europe-Narrative Review

Diabetic retinopathy (DR) is a leading cause of preventable vision impairment and blindness in the European Region. Despite the fact that almost all European countries have some kind of prophylactic eye examination for people with diabetes, the examinations are not properly arranged and are not organized according to the principles of screening in medicine. In 2021, the current COVID-19 pandemic moved telemedicine to the forefront healthcare services. Due to that, a lot more patients could benefit from comfortable and faster access to ophthalmology specialist care. This study aimed to conduct a narrative literature review on current DR screening programs and registries in the European Union for the last 20 years. With the implementation of telemedicine in daily medical practice, performing screening programs became much more attainable. Remote assessment of retinal pictures simultaneously saves countries time, money, and other resources.

Multidisease Deep Learning Neural Network for the Diagnosis of Corneal Diseases

PURPOSE

To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images.

STUDY DESIGN

Development of a deep learning neural network diagnosis algorithm.

METHODS

A total of 158,220 AS-OCT images from 879 eyes of 478 subjects were used to develop and validate a classification deep network. After a quality check, the network was trained and validated using 134,460 images. We tested the network using a test set of consecutive patients involving 23,760 AS-OCT images of 132 eyes of 69 patients. The area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and F1 score and 95% confidence intervals (CIs) were computed.

RESULTS

The MDDN achieved eye-level AUROCs >0.99 (95% CI: 0.90, 1.0), AUPRCs > 0.96 (95% CI: 0.90, 1.0), and F1 scores > 0.90 (95% CI: 0.81, 1.0) for DES, FED, and KCN, respectively.

CONCLUSIONS

MDDN is a novel diagnostic tool for corneal diseases that can be used to automatically diagnose KCN, FED, and DES using only AS-OCT images.

A deep learning system for detecting diabetic retinopathy across the disease spectrum

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.

Diabetic retinopathy classification based on multipath CNN and machine learning classifiers

Eye care professionals generally use fundoscopy to confirm the occurrence of Diabetic Retinopathy (DR) in patients. Early DR detection and accurate DR grading are critical for the care and management of this disease. This work proposes an automated DR grading method in which features can be extracted from the fundus images and categorized based on severity using deep learning and Machine Learning (ML) algorithms. A Multipath Convolutional Neural Network (M-CNN) is used for global and local feature extraction from images. Then, a machine learning classifier is used to categorize the input according to the severity. The proposed model is evaluated across different publicly available databases (IDRiD, Kaggle (for DR detection), and MESSIDOR) and different ML classifiers (Support Vector Machine (SVM), Random Forest, and J48). The metrics selected for model evaluation are the False Positive Rate (FPR), Specificity, Precision, Recall, F1-score, K-score, and Accuracy. The experiments show that the best response is produced by the M-CNN network with the J48 classifier. The classifiers are evaluated across the pre-trained network features and existing DR grading methods. The average accuracy obtained for the proposed work is 99.62% for DR grading. The experiments and evaluation results show that the proposed method works well for accurate DR grading and early disease detection.

Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence

Purpose

Abnormalities of the running pattern of choroidal vessel have been reported in eyes with pachychoroid diseases. However, it is difficult for clinicians to judge the running pattern with high reproducibility. Thus, the purpose of this study was to compare the degree of concordance of the running pattern of the choroidal vessels between that determined by artificial intelligence (AI) to that determined by experienced clinicians.

Methods

The running pattern of the choroidal vessels in en face images of Haller’s layer of 413 normal and pachychoroid diseased eyes was classified as symmetrical or asymmetrical by human raters and by three supervised machine learning models; the support vector machine (SVM), Xception, and random forest models. The data from the human raters were used as the supervised data. The accuracy rates of the human raters and the certainty of AI’s answers were compared using confidence scores (CSs).

Results

The choroidal vascular running pattern could be determined by each AI model with an area under the curve better than 0.94. The random forest method was able to discriminate with the highest accuracy among the three AIs. In the CS analyses, the percentage of certainty was highest (66.4%) and that of uncertainty was lowest (6.1%) in the agreement group. On the other hand, the rate of uncertainty was highest (27.3%) in the disagreement group.

Conclusion

AI algorithm can automatically classify with ambiguous criteria the presence or absence of a symmetrical blood vessel running pattern of the choroid. The classification was as good as that of supervised humans in accuracy and reproducibility.

Validation of Automated Screening for Referable Diabetic Retinopathy With an Autonomous Diagnostic Artificial Intelligence System in a Spanish Population

PURPOSE

The purpose of this study is to compare the diagnostic performance of an autonomous artificial intelligence (AI) system for the diagnosis of referable diabetic retinopathy (RDR) to manual grading by Spanish ophthalmologists.

METHODS

Subjects with type 1 and 2 diabetes participated in a diabetic retinopathy (DR) screening program in 2011 to 2012 in Valencia (Spain), and two images per eye were collected according to their standard protocol. Mydriatic drops were used in all patients. Retinal images-one disc and one fovea centered-were obtained under the Medical Research Ethics Committee approval and de-identified. Exams were graded by the autonomous AI system (IDx-DR, Coralville, Iowa, United States), and manually by masked ophthalmologists using adjudication. The outputs of the AI system and manual adjudicated grading were compared using sensitivity and specificity for diagnosis of both RDR and vision-threatening diabetic retinopathy (VTDR).

RESULTS

A total of 2680 subjects were included in the study. According to manual grading, prevalence of RDR was 111/2680 (4.14%) and of VTDR was 69/2680 (2.57%). Against manual grading, the AI system had a 100% (95% confidence interval [CI]: 97%-100%) sensitivity and 81.82% (95% CI: 80%-83%) specificity for RDR, and a 100% (95% CI: 95%-100%) sensitivity and 94.64% (95% CI: 94%-95%) specificity for VTDR.

CONCLUSION

Compared to manual grading by ophthalmologists, the autonomous diagnostic AI system had high sensitivity (100%) and specificity (82%) for diagnosing RDR and macular edema in people with diabetes in a screening program. Because of its immediate, point of care diagnosis, autonomous diagnostic AI has the potential to increase the accessibility of RDR screening in primary care settings.

Role of Oral Antioxidant Supplementation in the Current Management of Diabetic Retinopathy

Oxidative stress has been postulated as an underlying pathophysiologic mechanism of diabetic retinopathy (DR), the main cause of avoidable blindness in working-aged people. This review addressed the current daily clinical practice of DR and the role of antioxidants in this practice. A systematic review of the studies on antioxidant supplementation in DR patients was presented. Fifteen studies accomplished the inclusion criteria. The analysis of these studies concluded that antioxidant supplementation has a IIB level of recommendation in adult Type 1 and Type 2 diabetes mellitus subjects without retinopathy or mild-to-moderate nonproliferative DR without diabetic macular oedema as a complementary therapy together with standard medical care.

Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography

PURPOSE

The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images.

METHOD

The study included 491 ultra-wide-field fundus ophthalmoscopy and optical coherence tomography angiography (OCTA) images that passed an image-quality review and were graded as no apparent DR (NDR; 169 images), mild nonproliferative DR (NPDR; 76 images), moderate NPDR (54 images), severe NPDR (90 images), and proliferative DR (PDR; 102 images) by three retinal experts by the International Clinical Diabetic Retinopathy Severity Scale. The findings of tests 1 and 2 to identify no apparent diabetic retinopathy (NDR) and PDR, respectively, were then assessed. For each verification, Optos, OCTA, and Optos OCTA imaging scans with DCNN were performed.

RESULT

The Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and DR showed mean areas under the curve (AUC) of 0.79, 0.883, and 0.847; sensitivity rates of 80.9%, 83.9%, and 78.6%; and specificity rates of 55%, 71.6%, and 69.8%, respectively. Meanwhile, the Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and PDR showed mean AUC of 0.981, 0.928, and 0.964; sensitivity rates of 90.2%, 74.5%, and 80.4%; and specificity rates of 97%, 97%, and 96.4%, respectively.

CONCLUSION

The combination of Optos and OCTA imaging with DCNN could detect DR at desirable levels of accuracy and may be useful in clinical practice and retinal screening. Although the combination of multiple imaging techniques might overcome their individual weaknesses and provide comprehensive imaging, artificial intelligence in classifying multimodal images has not always produced accurate results.

Danish teleophthalmology platform reduces optometry referrals into the national eye care system

Objective

The purpose of this study was to evaluate the stratification of follow-up and referral pathways after implementation of a systematic cloud-based electronic-referral teleophthalmological service for optometry-initiated ocular posterior segment disease referrals to the Danish national eye care system.

Methods and Analysis

A retrospective cohort study was conducted in the period from 1 August 2018 to 31 July 2019. Patients with suspected ocular posterior segment disease reviewed by the telemedical ophthalmology service were included. The service stratified patients into the categories: no need for follow-up, follow-up by optometrist, follow-up by the telemedical service and referral to the national Danish eye care service.

Results

From a pool of 386 361 customers, 9938 patients were enrolled into this study. 19.5% of all patients were referred to the Danish national eye care system, while 80.5% of the patients in the telemedical service were not, in the period from 1 August 2018 to 31 July 2019. 14.4% of the optometrist referrals did not need any follow-up, while a majority of 66.1% needed some follow-up either by the optometrist themselves or within the telemedical service.

Conclusion

Optometrist posterior segment disease referrals can be considerably reduced with a risk stratified approach and optimal use of technology. New models can improve and streamline the healthcare system.

Artificial intelligence (AI) in medicine as a strategic valuable tool

Humans' creativity led to machines that outperform human capabilities in terms of workload, effectiveness, precision, endurance, strength, and repetitiveness. It has always been a vision and a way to transcend the existence and to give more sense to life, which is precious. The common denominator of all these creations was that they were meant to replace, enhance or go beyond the mechanical capabilities of the human body. The story takes another bifurcation when Alan Turing introduced the concept of a machine that could think, in 1950. Artificial intelligence, presented as a term in 1956, describes the use of computers to imitate intelligence and critical thinking comparable to humans. However, the revolution began in 1943, when artificial neural networks was an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with. Artificial intelligence is becoming a research focus and a tool of strategic value. The same observations apply in the field of healthcare, too. In this manuscript, we try to address key questions regarding artificial intelligence in medicine, such as what artificial intelligence is and how it works, what is its value in terms of application in medicine, and what are the prospects?

A 15 month experience with a primary care-based telemedicine screening program for diabetic retinopathy

Background

Using telemedicine for diabetic retinal screening is becoming popular especially amongst at-risk urban communities with poor access to care. The goal of the diabetic telemedicine project at Temple University Hospital is to improve cost-effective access to appropriate retinal care to those in need of close monitoring and/or treatment.

Methods

This will be a retrospective review of 15 months of data from March 2016 to May 2017. We will investigate how many patients were screened, how interpretable the photographs were, how often the photographs generated a diagnosis of diabetic retinopathy (DR) based on the screening photo, and how many patients followed-up for an exam in the office, if indicated.

Results

Six-hundred eighty-nine (689) digital retinal screening exams on 1377 eyes of diabetic patients were conducted in Temple’s primary care clinic. The majority of the photographs were read to have no retinopathy (755, 54.8%). Among all of the screening exams, 357 (51.8%) triggered a request for a referral to ophthalmology. Four-hundred forty-nine (449, 32.6%) of the photos were felt to be uninterpretable by the clinician. Referrals were meant to be requested for DR found in one or both eyes, inability to assess presence of retinopathy in one or both eyes, or for suspicion of a different ophthalmic diagnosis. Sixty-seven patients (9.7%) were suspected to have another ophthalmic condition based on other findings in the retinal photographs. Among the 34 patients that were successfully completed a referral visit to Temple ophthalmology, there was good concordance between the level of DR detected by their screening fundus photographs and visit diagnosis.

Conclusions

Although a little more than half of the patients did not have diabetic eye disease, about half needed a referral to ophthalmology. However, only 9.5% of the referral-warranted exams actually received an eye exam. Mere identification of referral-warranted diabetic retinopathy and other ophthalmic conditions is not enough. A successful telemedicine screening program must close the communication gap between screening and diagnosis by reviewer to provide timely follow-up by eye care specialists.

Machine learning-based heart disease prediction system for Indian population: An exploratory study done in South India

Background

In India, huge mortality occurs due to cardiovascular diseases (CVDs) as these diseases are not diagnosed in early stages. Machine learning (ML) algorithms can be used to build efficient and economical prediction system for early diagnosis of CVDs in India.

Methods

A total of 1670 anonymized medical records were collected from a tertiary hospital in South India. Seventy percent of the collected data were used to train the prediction system. Five state-of-the-art ML algorithms (k-Nearest Neighbours, Naïve Bayes, Logistic Regression, AdaBoost and Random Forest [RF]) were applied using Python programming language to develop the prediction system. The performance was evaluated over remaining 30% of data. The prediction system was later deployed in the cloud for easy accessibility via Internet.

Results

ML effectively predicted the risk of heart disease. The best performing (RF) prediction system correctly classified 470 out of 501 medical records thus attaining a diagnostic accuracy of 93.8%. Sensitivity and specificity were observed to be 92.8% and 94.6%, respectively. The prediction system attained positive predictive value of 94% and negative predictive value of 93.6%. The prediction model developed in this study can be accessed at http://das.southeastasia.cloudapp.azure.com/predict/.

Conclusions

ML-based prediction system developed in this study performs well in early diagnosis of CVDs and can be accessed via Internet. This study offers promising results suggesting potential use of ML-based heart disease prediction system as a screening tool to diagnose heart diseases in primary healthcare centres in India, which would otherwise get undetected.

Evaluation of a novel artificial intelligence-based screening system for diabetic retinopathy in community of China: a real-world study

PURPOSE

To evaluate the performance of an AI-based diabetic retinopathy (DR) grading model in real-world community clinical setting.

METHODS

Participants with diabetes on record in the chosen community were recruited by health care staffs in a primary clinic of Zhengzhou city, China. Retinal images were prospectively collected during December 2018 and April 2019 based on intent-to-screen principle. A pre-validated AI system based on deep learning algorithm was deployed to screen DR graded according to the International Clinical Diabetic Retinopathy scale. Kappa value of DR severity, the sensitivity, specificity of detecting referable DR (RDR) and any DR were generated based on the standard of the majority manual grading decision of a retina specialist panel.

RESULTS

Of the 193 eligible participants, 173 (89.6%) were readable with at least one eye image. Mean [SD] age was 69.3 (9.0) years old. Total of 321 eyes (83.2%) were graded both by AI and the specialist panel. The κ value in eye image grading was 0.715. The sensitivity, specificity and area under curve for detection of RDR were 84.6% (95% CI: 54.6- 98.1%), 98.0% (95% CI: 94.3-99.6%) and 0.913 (95% CI: 0.797-1.000), respectively. For detection of any DR, the upper indicators were 90.0% (95% CI: 68.3-98.8), 96.6% (95% CI: 92.1-98.9) and 0.933 (95% CI: 0.933-1.000), respectively.

CONCLUSION

The AI system showed relatively good consistency with ophthalmologist diagnosis in DR grading, high specificity and acceptable sensitivity for identifying RDR and any DR.

TRANSLATIONAL RELEVANCE

It is feasible to apply AI-based DR screening in community.

PRECIS

Deployed in community real-world clinic setting, AI-based DR screening system showed high specificity and acceptable sensitivity in identifying RDR and any DR. Good DR diagnostic consistency was found between AI and manual grading. These prospective evidences were essential for regulatory approval.

ARTEFICIAL INTELLIGENCE IN DIABETIC RETINOPATHY SCREENING. A REVIEW

OBJECTIVE

The aim of this comprehensive paper is to acquaint the readers with evaluation of the retinal images using the arteficial intelligence (AI). Main focus of the paper is diabetic retinophaty (DR) screening. The basic principles of the artificial intelligence and algorithms that are already used in clinical practice or are shortly before approval will be described.

METHODOLOGY

Describing the basic characteristics and mechanisms of different approaches to the use of AI and subsequently literary minireview clarifying the current state of knowledge in the area.

RESULTS

Modern systems for screening diabetic retinopathy using deep neural networks achieve a sensitivity and specificity of over 80 % in most published studies. The results of specific studies vary depending on the definition of the gold standard, number of images tested and on the evaluated parameters.

CONCLUSION

Evaluation of images using AI will speed up and streamline the diagnosis of DR. The use of AI will allow to keep the quality of the eye care at least on the same level despite the raising number of the patients with diabetes.

[Artificial intelligence in management of macular edema : Opportunities and challenges]

Macular edema occurs in a wide variety of ophthalmological diseases. The diagnostics and treatment are an important part of modern ophthalmology. Due to the continuous development, artificial intelligence (AI) offers many opportunities to improve the management of macular edema. This article provides the readership with an overview of this interesting topic.

Use in clinical practice of an automated screening method of diabetic retinopathy that can be derived using a diagnostic artificial intelligence system

BACKGROUND AND OBJECTIVE

To compare the diagnostic performance of an autonomous diagnostic artificial intelligence (AI) system for the diagnosis of derivable diabetic retinopathy (RDR) with manual classification.

MATERIALS AND METHODS

Patients with type 1 and type 2 diabetes participated in a diabetic retinopathy (DR) screening program between 2011-2012. 2 images of each eye were collected. Unidentifiable retinal images were obtained, one centered on the disc and one on the fovea. The exams were classified with the autonomous AI system and manually by anonymous ophthalmologists. The results of the AI system and manual classification were compared in terms of sensitivity and specificity for the diagnosis of both (RDR) and diabetic retinopathy with decreased vision (VTDR).

RESULTS

10,257 retinal inages of 5,630 eyes of 2,680 subjects were included. According to the manual classification, the prevalence of RDR was 4.14% and that of VTDR 2.57%. The AI system recorded 100% (95% CI: 97-100%) sensitivity and 81.82% (95% CI: 80 -83%) specificity for RDR, and 100% (95% CI: 95-100%) of sensitivity and 94.64% (95% CI: 94-95%) of specificity for VTDR.

CONCLUSIONS

Compared to the manual classification, the autonomous diagnostic AI system registered a high sensitivity (100%) and specificity (82%) in the diagnosis of RDR and macular edema in people with diabetes. Due to its immediate diagnosis, the autonomous diagnostic AI system can increase the accessibility of RDR screening in primary care settings.

Artificial intelligence CT screening model for thyroid-associated ophthalmopathy and tests under clinical conditions

PURPOSE

Thyroid-associated ophthalmopathy (TAO) might lead to blindness and orbital deformity. The early diagnosis and treatment are conducive to control disease progression, but currently, there is no effective screening method. The present study aimed to introduce an artificial intelligence (AI) model for screening and testing the model with TAO patients under clinical conditions.

METHODS

A total of 1435 computed tomography (CT) scans were obtained from the hospital. These CT scans were preprocessed by resampling and extracting the region of interest. CT from 193 TAO patients and 715 healthy individuals were adopted for three-dimensional (3D)-ResNet model training, and 49 TAO patients and 178 healthy people were adopted for external verification. Data from 150 TAO patients and 150 healthy people were utilized for application tests under clinical conditions, including non-inferiority experiments and diagnostic tests, respectively.

RESULTS

In the external verification of the model, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.919, indicating a satisfactory classification effect. The accuracy, sensitivity, and specificity were 0.87, 088, and 0.85, respectively. In non-inferiority experiments: the accuracy was 85.67% in the AI group and 84.33% in the resident group. The model passed both non-inferiority experiments (p = 0.001) and diagnostic test (the AI group sensitivity = 0.87 and specificity = 0.84%).

CONCLUSIONS

A promising orbital CT-based TAO screening AI model was established and passed application tests under clinical conditions. This may provide a new TAO screening tool with further validation.