Abstract
The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.
Highlights
Ophthalmology and the field of retinal diseases relies heavily on information derived from ophthalmic imaging for diagnosis, treatment and disease activity monitoring
Prognosis, treatment initiation, and therapeutic response. We describe these measured features that are found to have clinical applications for review, we describe these measured features that are found to have clinical applications the management of disease as “quantitative imaging biomarkers”, which may serve as for the management of disease as “quantitative imaging biomarkers”, which may serve as objective tools for the future in the context of diabetic eye disease and age-related macular objective tools for the future in the context of diabetic eye disease and age-related macular degeneration (Figure 1)
Studies that included only qualitative findings or that focused on pathologies other than diabetic eye disease and Age-Related Macular Degeneration (AMD) were not included in this study
Summary
Ophthalmology and the field of retinal diseases relies heavily on information derived from ophthalmic imaging for diagnosis, treatment and disease activity monitoring. Current imaging systems provide outstanding details of disease burdens and the impact of different retinal diseases This information has been utilized in a qualitative manner and relies on an ophthalmologist’s interpretation and expertise. It provides opportunity to physicians to interpret images better regarding individualized therapy, surveillance, diagnosis, and prognosis [25] These advanced image analysis techniques have been denosis, and prognosis [25]. The role of radiomics features in predicting therapeutic response sponse and prognosis in ophthalmic diseases is emerging as an exciting opportunity for and prognosis in ophthalmic diseases is emerging as an exciting opportunity for enhanced enhanced personalized care [17,18] The boom in this image analysis space over the past decade has made it possible to The boom in this image analysis space over the past decade has made it possible to automate the quantification and interpretation of ophthalmic imaging biomarkers. Studies that included only qualitative findings or that focused on pathologies other than diabetic eye disease and AMD were not included in this study
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