Abstrakt
Retina provides a unique window to observe blood vessels and neural tissue in vivo. Ophthalmologists diagnose retinal diseases by identifying specific signs on the retina. Retinal photography has contributed to enable quantitative retinal image analysis, especially after the digital fundus camera introduced. There are huge potentials and expectations in RIA to make retinal assessment more precise, reliable, and quantifiable beyond clinical diagnosis from preclinical screening to diagnosis to support clinical decision making. This chapter overviewed five key areas that have been enhanced with RIA providing a non-invasive tool to probe the role of the microvasculature in the development of clinical eye diseases and systemic diseases. There is an expectation that repetitive screening work can be replaced with automated grading system without compensating high accuracy. Deep learning classification might provide a new insight into what clinicians at preclinical stage. RIA holds a key to improve healthcare for both ophthalmological and systemic disease.
Originalsprog | Udefineret/Ukendt |
---|---|
Titel | Computational Retinal Image Analysis : Tools, Applications and Perspectives |
Redaktører | Emanuele Trucco, Tom MacGillivray, Yanwu Xu |
Forlag | Academic Press |
Publikationsdato | 2019 |
Sider | 5 - 17 |
ISBN (Trykt) | 978-0-08-102816-2 |
DOI | |
Status | Udgivet - 2019 |
Emneord
- Retina
- Fundus photography
- Optical coherence tomography
- Computer aided diagnosis
- Biomarker
- Diabetic retinopathy
- Retinal vessel caliber