The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy

Künstliche Intelligenz (KI) zur Früherkennung von Netzhauterkrankungen

Künstliche Intelligenz (KI) hat sich zu einem transformativen Werkzeug auf dem Gebiet der Augenheilkunde entwickelt und revolutioniert die Diagnose und Behandlung von Krankheiten. Diese Arbeit gibt einen umfassenden Überblick über KI-Anwendungen bei verschiedenen Netzhauterkrankungen und zeigt ihr Potenzial, die Effizienz von Vorsorgeuntersuchungen zu erhöhen, Frühdiagnosen zu erleichtern und die Patientenergebnisse zu verbessern. Wir erklären die grundlegenden Konzepte der KI, einschließlich des maschinellen Lernens (ML) und des Deep Learning (DL), und deren Anwendung in der Augenheilkunde und heben die Bedeutung von KI-basierten Lösungen bei der Bewältigung der Komplexität und Variabilität von Netzhauterkrankungen hervor. Wir gehen auch auf spezifische Anwendungen der KI im Zusammenhang mit Netzhauterkrankungen wie diabetischer Retinopathie (DR), altersbedingter Makuladegeneration (AMD), makulärer Neovaskularisation, Frühgeborenen-Retinopathie (ROP), retinalem Venenverschluss (RVO), hypertensiver Retinopathie (HR), Retinopathia pigmentosa, Morbus Stargardt, Morbus Best (Best’sche vitelliforme Makuladystrophie) und Sichelzellenretinopathie ein. Wir konzen­trieren uns auf die aktuelle Landschaft der KI-Technologien, einschließlich verschiedener KI-Modelle, ihrer Leistungsmetriken und klinischen Implikationen. Darüber hinaus befassen wir uns mit den Herausforderungen und Schwierigkeiten bei der Integration von KI in die klinische Praxis, einschließlich des «Black-Box-Phänomens», der Verzerrungen bei der Darstellung von Daten und der Einschränkungen im Zusammenhang mit der ganzheitlichen Bewertung von Patienten. Abschließend wird die kollaborative Rolle der KI an der Seite des medizinischen Fachpersonals hervorgehoben, wobei ein synergetischer Ansatz für die Erbringung von Gesundheitsdienstleistungen befürwortet wird. Es wird betont, wie wichtig es ist, KI als Ergänzung und nicht als Ersatz für menschliche Expertise einzusetzen, um ihr Potenzial zu maximieren, die Gesundheitsversorgung zu revolutionieren, Ungleichheiten in der Gesundheitsversorgung zu verringern und die Patientenergebnisse in der sich entwickelnden medizinischen Landschaft zu verbessern.

The efficacy of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis

BACKGROUND

To evaluate the efficacy of artificial intelligence (AI) in screening for diabetic retinopathy (DR) using fundus images and optical coherence tomography (OCT) in comparison to traditional screening methods.

METHODS

This systematic review was registered with PROSPERO (ID: CRD42024560750). Systematic searches were conducted in PubMed Medline, Cochrane Central, ScienceDirect, and Web of Science using keywords such as "diabetic retinopathy," "screening," and "artificial intelligence." Only studies published in English from 2019 to July 22, 2024, were considered. We also manually reviewed the reference lists of relevant reviews. Two independent reviewers assessed the risk of bias using the QUADAS-2 tool, resolving disagreements through discussion with the principal investigator. Meta-analysis was performed using MetaDiSc software (version 1.4). To calculate combined sensitivity, specificity, summary receiver operating characteristic (SROC) plots, forest plots, and subgroup analyses were performed according to clinician type (ophthalmologists vs. retina specialists) and imaging modality (fundus images vs. fundus images + OCT).

RESULTS

18 studies were included. Meta-analysis showed that AI systems demonstrated superior diagnostic performance compared to doctors, with the pooled sensitivity, specificity, diagnostic odds ratio, and Cochrane Q index of the AI being 0.877, 0.906, 0.94, and 153.79 accordingly. The Fagan nomogram analysis further confirmed the strong diagnostic value of AI. Subgroup analyses revealed that factors like imaging modality, and doctor expertise can influence diagnostic performance.

CONCLUSION

AI systems have demonstrated strong diagnostic performance in detecting diabetic retinopathy, with sensitivity and specificity comparable to or exceeding traditional clinicians.

Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis

BACKGROUND

In recent years, artificial intelligence and machine learning algorithms have been used more extensively to diagnose diabetic retinopathy and other diseases. Still, the effectiveness of these methods has not been thoroughly investigated. This study aimed to evaluate the performance and limitations of machine learning and deep learning algorithms in detecting diabetic retinopathy.

METHODS

This study was conducted based on the PRISMA checklist. We searched online databases, including PubMed, Scopus, and Google Scholar, for relevant articles up to September 30, 2023. After the title, abstract, and full-text screening, data extraction and quality assessment were done for the included studies. Finally, a meta-analysis was performed.

RESULTS

We included 76 studies with a total of 1,371,517 retinal images, of which 51 were used for meta-analysis. Our meta-analysis showed a significant sensitivity and specificity with a percentage of 90.54 (95%CI [90.42, 90.66], P < 0.001) and 78.33% (95%CI [78.21, 78.45], P < 0.001). However, the AUC (area under curvature) did not statistically differ across studies, but had a significant figure of 0.94 (95% CI [- 46.71, 48.60], P = 1).

CONCLUSIONS

Although machine learning and deep learning algorithms can properly diagnose diabetic retinopathy, their discriminating capacity is limited. However, they could simplify the diagnosing process. Further studies are required to improve algorithms.

Retinal Vein Occlusion-Background Knowledge and Foreground Knowledge Prospects-A Review

Thrombosis of retinal veins is one of the most common retinal vascular diseases that may lead to vascular blindness. The latest epidemiological data leave no illusions that the burden on the healthcare system, as impacted by patients with this diagnosis, will increase worldwide. This obliges scientists to search for new therapeutic and diagnostic options. In the 21st century, there has been tremendous progress in retinal imaging techniques, which has facilitated a better understanding of the mechanisms related to the development of retinal vein occlusion (RVO) and its complications, and consequently has enabled the introduction of new treatment methods. Moreover, artificial intelligence (AI) is likely to assist in selecting the best treatment option for patients in the near future. The aim of this comprehensive review is to re-evaluate the old but still relevant data on the RVO and confront them with new studies. The paper will provide a detailed overview of diagnosis, current treatment, prevention, and future therapeutic possibilities regarding RVO, as well as clarifying the mechanism of macular edema in this disease entity.

RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis

Visual impairments and blindness are major public health concerns globally. Effective eye disease screening aided by artificial intelligence (AI) is a promising countermeasure, although it is challenged by practical constraints such as poor image quality in community screening. The recently developed ophthalmic foundation model RETFound has shown higher accuracy in retinal image recognition tasks. This study developed an RETFound-enhanced deep learning (DL) model for multiple-eye disease screening using real-world images from community screenings. Our results revealed that our DL model improved the sensitivity and specificity by over 15% compared with commercial models. Our model also shows better generalisation ability than AI models developed using traditional processes. Additionally, decision curve analysis underscores the higher net benefit of employing our model in both urban and rural settings in China. These findings indicate that the RETFound-enhanced DL model can achieve a higher net benefit in community-based screening, advocating its adoption in low- and middle-income countries to address global eye health challenges.

Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM

To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs).

METHODS

Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts.

RESULTS

The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior.

CONCLUSION

The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Artificial intelligence in retinal imaging: current status and future prospects

INTRODUCTION

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

AREAS COVERED

This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature.

EXPERT OPINION

The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data

BACKGROUND

Community-based telemedicine screening for diabetic retinopathy (DR) has been highly recommended worldwide. However, evidence from low- and middle-income countries (LMICs) on the choice between artificial intelligence (AI)-based and manual grading-based telemedicine screening is inadequate for policy making.

OBJECTIVE

The aim of this study was to test whether the AI model is more worthwhile than manual grading in community-based telemedicine screening for DR in the context of labor costs in urban China.

METHODS

We conducted cost-effectiveness and cost-utility analyses by using decision-analytic Markov models with 30 one-year cycles from a societal perspective to compare the cost, effectiveness, and utility of 2 scenarios in telemedicine screening for DR: manual grading and an AI model. Sensitivity analyses were performed. Real-world data were obtained mainly from the Shanghai Digital Eye Disease Screening Program. The main outcomes were the incremental cost-effectiveness ratio (ICER) and the incremental cost-utility ratio (ICUR). The ICUR thresholds were set as 1 and 3 times the local gross domestic product per capita.

RESULTS

The total expected costs for a 65-year-old resident were US $3182.50 and US $3265.40, while the total expected years without blindness were 9.80 years and 9.83 years, and the utilities were 6.748 quality-adjusted life years (QALYs) and 6.753 QALYs in the AI model and manual grading, respectively. The ICER for the AI-assisted model was US $2553.39 per year without blindness, and the ICUR was US $15,216.96 per QALY, which indicated that AI-assisted model was not cost-effective. The sensitivity analysis suggested that if there is an increase in compliance with referrals after the adoption of AI by 7.5%, an increase in on-site screening costs in manual grading by 50%, or a decrease in on-site screening costs in the AI model by 50%, then the AI model could be the dominant strategy.

CONCLUSIONS

Our study may provide a reference for policy making in planning community-based telemedicine screening for DR in LMICs. Our findings indicate that unless the referral compliance of patients with suspected DR increases, the adoption of the AI model may not improve the value of telemedicine screening compared to that of manual grading in LMICs. The main reason is that in the context of the low labor costs in LMICs, the direct health care costs saved by replacing manual grading with AI are less, and the screening effectiveness (QALYs and years without blindness) decreases. Our study suggests that the magnitude of the value generated by this technology replacement depends primarily on 2 aspects. The first is the extent of direct health care costs reduced by AI, and the second is the change in health care service utilization caused by AI. Therefore, our research can also provide analytical ideas for other health care sectors in their decision to use AI.

Advances in artificial intelligence models and algorithms in the field of optometry

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.