Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus

Chou, Yu-Bai; Kale, Aditya U; Lanzetta, Paolo; Aslam, Tariq; Barratt, Jane; Danese, Carla; Eldem, Bora; Eter, Nicole; Gale, Richard; Korobelnik, Jean‐François; Kozák, Igor; Li, Xiaorong; Li, Xiaoxin; Loewenstein, Anat; Ruamviboonsuk, Paisan; Sakamoto, Taiji; Ting, Daniel Shu Wei; Wijngaarden, Peter van; Waldstein, Sebastian M.; Wong, David T.; Wu, Lihteh; Zapata, Miguel Á.; Zarranz‐Ventura, Javier

doi:10.1097/icu.0000000000000979

Cited by 10 publications

(4 citation statements)

References 76 publications

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“…One key factor to highlight is the accessibility and affordability of retinal imaging technology. Several fully automated retinal imaging systems are already available in the market [ 75 ], and as technology advances, their cost-effectiveness is likely to improve [ 75 ]. These developments make it increasingly feasible for retinal vessel examination to become a routine adjunct for primary care doctors.…”

Section: Disscusionmentioning

confidence: 99%

“…These developments make it increasingly feasible for retinal vessel examination to become a routine adjunct for primary care doctors. For example, in primary care settings, where patients often receive initial assessments for various health concerns, retinal imaging could serve as a valuable addition to the diagnostic toolbox [ 75 ]. Imagine a scenario where a patient visits their family physician for a routine check-up.…”

Section: Disscusionmentioning

confidence: 99%

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Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

Tan,

Kang,

Lee

et al. 2024

Eye and Vis

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Background Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina’s unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. Main text This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson’s disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. Conclusion AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina’s unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

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Section: Disscusionmentioning

confidence: 99%

Section: Disscusionmentioning

confidence: 99%

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

Tan,

Kang,

Lee

et al. 2024

Eye and Vis

View full text Add to dashboard Cite

show abstract

“…Several AI-based algorithms and devices have received class IIa approval in the European Union (EU), including LumineticsCore, EyeArt, RetmarkerDR by Retmarker, SELENA+ by eyRIS, Automated Retinal Disease Assessment (ARDA) by Verily Life Sciences LLC, Medios AI by Remidio, OphtAI by Evolucare, VUNO Med-Fundus AI by VUNO Inc., and RetCAD by Thirona (Table 2) [142,143].…”

Section: Milestones and Market Approvals For Ai Algorithmsmentioning

confidence: 99%

Advances in Structural and Functional Retinal Imaging and Biomarkers for Early Detection of Diabetic Retinopathy

Zhang,

Deng,

Paulus

2024

Biomedicines

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Diabetic retinopathy (DR), a vision-threatening microvascular complication of diabetes mellitus (DM), is a leading cause of blindness worldwide that requires early detection and intervention. However, diagnosing DR early remains challenging due to the subtle nature of initial pathological changes. This review explores developments in multimodal imaging and functional tests for early DR detection. Where conventional color fundus photography is limited in the field of view and resolution, advanced quantitative analysis of retinal vessel traits such as retinal microvascular caliber, tortuosity, and fractal dimension (FD) can provide additional prognostic value. Optical coherence tomography (OCT) has also emerged as a reliable structural imaging tool for assessing retinal and choroidal neurodegenerative changes, which show potential as early DR biomarkers. Optical coherence tomography angiography (OCTA) enables the evaluation of vascular perfusion and the contours of the foveal avascular zone (FAZ), providing valuable insights into early retinal and choroidal vascular changes. Functional tests, including multifocal electroretinography (mfERG), visual evoked potential (VEP), multifocal pupillographic objective perimetry (mfPOP), microperimetry, and contrast sensitivity (CS), offer complementary data on early functional deficits in DR. More importantly, combining structural and functional imaging data may facilitate earlier detection of DR and targeted management strategies based on disease progression. Artificial intelligence (AI) techniques show promise for automated lesion detection, risk stratification, and biomarker discovery from various imaging data. Additionally, hematological parameters, such as neutrophil–lymphocyte ratio (NLR) and neutrophil extracellular traps (NETs), may be useful in predicting DR risk and progression. Although current methods can detect early DR, there is still a need for further research and development of reliable, cost-effective methods for large-scale screening and monitoring of individuals with DM.

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“…One concern in the use of LLMs for medical applications is the lack of reproducibility, as these generative models may not consistently provide the same answers, potentially impacting the reliability of their outputs in clinical settings. Addressing these challenges will be essential to fully realize the potential benefits of large language models in ophthalmology and to ensure their responsible and ethical implementation in patient care (48).…”

Section: Challengesmentioning

confidence: 99%

Exploring large language model for next generation of artificial intelligence in ophthalmology

Jin,

Yuan,

et al. 2023

Front. Med.

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In recent years, ophthalmology has advanced significantly, thanks to rapid progress in artificial intelligence (AI) technologies. Large language models (LLMs) like ChatGPT have emerged as powerful tools for natural language processing. This paper finally includes 108 studies, and explores LLMs’ potential in the next generation of AI in ophthalmology. The results encompass a diverse range of studies in the field of ophthalmology, highlighting the versatile applications of LLMs. Subfields encompass general ophthalmology, retinal diseases, anterior segment diseases, glaucoma, and ophthalmic plastics. Results show LLMs’ competence in generating informative and contextually relevant responses, potentially reducing diagnostic errors and improving patient outcomes. Overall, this study highlights LLMs’ promising role in shaping AI’s future in ophthalmology. By leveraging AI, ophthalmologists can access a wealth of information, enhance diagnostic accuracy, and provide better patient care. Despite challenges, continued AI advancements and ongoing research will pave the way for the next generation of AI-assisted ophthalmic practices.

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Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus

Cited by 10 publications

References 76 publications

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

Advances in Structural and Functional Retinal Imaging and Biomarkers for Early Detection of Diabetic Retinopathy

Exploring large language model for next generation of artificial intelligence in ophthalmology

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