Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases

Abstract

Retinal images provide a non-invasive and accessible means to directly visualize human blood vessels and nerve fibers. Growing studies have investigated the intricate microvascular and neural circuitry within the retina, its interactions with other systemic vascular and nervous systems, and the link between retinal biomarkers and various systemic diseases. Using the eye to study systemic health, based on these connections, has been given a term as oculomics. Advancements in artificial intelligence (AI) technologies, particularly deep learning, have further increased the potential impact of this study. Leveraging these technologies, retinal analysis has demonstrated potentials in detecting numerous diseases, including cardiovascular diseases, central nervous system diseases, chronic kidney diseases, metabolic diseases, endocrine disorders, and hepatobiliary diseases. AI-based retinal imaging, which incorporates established modalities such as digital color fundus photographs, optical coherence tomography (OCT) and OCT angiography, as well as emerging technologies like ultra-wide field imaging, shows great promises in predicting systemic diseases. This provides a valuable opportunity for systemic diseases screening, early detection, prediction, risk stratification, and personalized prognostication. As the AI and big data research field grows, with the mission of transforming healthcare, they also face numerous challenges and limitations both in data and technology. The application of natural language processing framework, large language model, and other generative AI techniques presents both opportunities and concerns that require careful consideration. In this review, we not only summarize key studies on AI-enhanced retinal imaging for predicting systemic diseases but also underscore the significance of these advancements in transforming healthcare. By highlighting the remarkable progress made thus far, we provide a comprehensive overview of state-of-the-art techniques and explore the opportunities and challenges in this rapidly evolving field. This review aims to serve as a valuable resource for researchers and clinicians, guiding future studies and fostering the integration of AI in clinical practice.

Keywords: artificial intelligence; color fundus photos.; deep learning; retinal imaging; systemic prediction.

Figure 1

The overview of how AI-enhanced retinal imaging predicting systemic diseases and the future considerations. Based on various modalities of available retinal imaging, through the pre-trained AI algorithms, we can predict risk factors, traditional variables and diseases diagnosis, which already applied in disorders from nearly all systems, including cardiovascular, metabolic, hematological, renal, hepatobiliary, psychiatric, cerebrovascular, neurodegenerative and immune diseases. The prediction includes not only diagnosis, but also early detection, incident and prognosis of the disorders based on the training on longitudinal data. The emerging new era of generative AI brings promising opportunities on medical application and healthcare transforming times. CFP: color fundus photo; OCT: optical coherence tomography; OCTA: optical coherence tomography angiography; UWF: ultra-wide field; AI: artificial intelligence; ML: machine learning; DL: deep learning.

Figure 2

Systemic disorders diagnosed on eye images developed AI models. Previous AI algorithms trained on EHR, individual information, and retinal images of certain population, that sets good basics to the coming new AI models. With the development of generative AI, multimodality new VLFMs may contain text, voice, images and video, linking previous AI models into a friendly human-computer interaction mode, applying oculomics studies into the GMAI system, potentially transforming ophthalmology healthcare. EHR: electronic health record; AI: artificial intelligence; LLM: large language model; VFM: vision foundational models; VLFM: vision-language foundational models; GMAI: generalist medical artificial intelligence.