Assessment of area and structural irregularity of retinal layers in diabetic retinopathy using machine learning and image processing techniques

Autor: Hamid Riazi-Esfahani, Behzad Jafari, Hossein Azimi, Masoud Rahimi, Jamshid Saeidian, Parnia Pouya, Hooshang Faghihi, Arash Mirzaei, Esmaeil Asadi Khameneh, Elias Khalili Pour
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-024-54535-6
Popis: Abstract Diabetes retinopathy prevention necessitates early detection, monitoring, and treatment. Non-invasive optical coherence tomography (OCT) shows structural changes in the retinal layer. OCT image evaluation necessitates retinal layer segmentation. The ability of our automated retinal layer segmentation to distinguish between normal, non-proliferative (NPDR), and proliferative diabetic retinopathy (PDR) was investigated in this study using quantifiable biomarkers such as retina layer smoothness index (SI) and area (S) in horizontal and vertical OCT images for each zone (fovea, superior, inferior, nasal, and temporal). This research includes 84 eyes from 57 individuals. The study shows a significant difference in the Area (S) of inner nuclear layer (INL) and outer nuclear layer (ONL) in the horizontal foveal zone across the three groups (p
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