AI Medical Compendium Journal:
Progress in retinal and eye research

Showing 1 to 10 of 11 articles

AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed.

Progress in retinal and eye research
Recent developments in artificial intelligence (AI) have seen a proliferation of algorithms that are now capable of predicting a range of systemic diseases from retinal images. Unlike traditional retinal disease detection AI models which are trained ...

AI in the clinical management of GA: A novel therapeutic universe requires novel tools.

Progress in retinal and eye research
Regulatory approval of the first two therapeutic substances for the management of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) is a major breakthrough following failure of numerous previous trials. However, in the absen...

The AI revolution in glaucoma: Bridging challenges with opportunities.

Progress in retinal and eye research
Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However,...

Assessment of angle closure disease in the age of artificial intelligence: A review.

Progress in retinal and eye research
Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disea...

Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives.

Progress in retinal and eye research
When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount...

Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice.

Progress in retinal and eye research
An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite a...

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

Progress in retinal and eye research
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 exam...

Artificial intelligence in OCT angiography.

Progress in retinal and eye research
Optical coherence tomographic angiography (OCTA) is a non-invasive imaging modality that provides three-dimensional, information-rich vascular images. With numerous studies demonstrating unique capabilities in biomarker quantification, diagnosis, and...

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective.

Progress in retinal and eye research
The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital in...

Deep learning in ophthalmology: The technical and clinical considerations.

Progress in retinal and eye research
The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for ...