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

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 < 0.001). In the horizontal scan, there is a significant difference in the smoothness index (SI) of the inner plexiform layer (IPL) and the upper border of the outer plexiform layer (OPL) among three groups (p < 0.05). There is also a significant difference in the area (S) of the OPL in the foveal zone among the three groups (p = 0.003). The area (S) of the INL in the foveal region of horizontal slabs performed best for distinguishing diabetic patients (NPDR and PDR) from normal individuals, with an accuracy of 87.6%. The smoothness index (SI) of IPL in the nasal zone of horizontal foveal slabs was the most accurate at 97.2% in distinguishing PDR from NPDR. The smoothness index of the top border of the OPL in the nasal zone of horizontal slabs was 84.1% accurate in distinguishing NPDR from PDR. Smoothness index of IPL in the temporal zone of horizontal slabs was 89.8% accurate in identifying NPDR from PDR patients. In conclusion, optical coherence tomography can assess the smoothness index and irregularity of the inner and outer plexiform layers, particularly in the nasal and temporal regions of horizontal foveal slabs, to distinguish non-proliferative from proliferative diabetic retinopathy. The evolution of diabetic retinopathy throughout severity levels and its effects on retinal layer irregularity need more study.

Assessment of retinal and choroidal vessel density and nerve fibre layer thickness changes after orbitotomy in patients with severe non-active thyroid orbitopathy: a prospective study.

PURPOSE: To evaluate the optical coherence tomography angiogram changes in non-active severe thyroid-related ophthalmopathy patients after cosmetic bone decompression. METHODS: Eighteen patients (25 eyes) with severe not active not compressive (NANC) TED who were candidates for decompression surgery for cosmetic reasons were included in this study, and a 3 × 3 mm macular scan was used to measure vessel density and RNFL thickness. Whole macular vessel density in its superficial, deep and choriocapillaris layers was evaluated. The following data were extracted for each of layers: superior and inferior hemispheres, fovea, parafoveal vessel density, its superior and inferior hemispheres, and temporal, superior, nasal and inferior quadrant. RESULTS: The mean RPC increased postoperatively, which was statistically significant in small vessels of peripapillary area (p-value = 0.045). The mean RNFL thickness decreased after surgery and it was statistically significant in the peripapillary (p-value = 0.032) and Inferior-Hemifield area (p-value = 0.036). The choriocapillaris changes were significant in Superior-Hemifield (p-value = 0.031) and Fovea (p-value = 0.03). CONCLUSION: Thyroid-associated orbitopathy patients have a tendency to decrease vascular density and correlated with disease activity more than stage of orbitopathy. There was not a strong and even discrepant result in linkage of RNFL thickness and other optic nerve function tests and TED patient status and it is needed to do studies with more epidemiologic power and same methodology of study to be more comparable.

Automated assessment of the smoothness of retinal layers in optical coherence tomography images using a machine learning algorithm.

Quantifying the smoothness of different layers of the retina can potentially be an important and practical biomarker in various pathologic conditions like diabetic retinopathy. The purpose of this study is to develop an automated machine learning algorithm which uses support vector regression method with wavelet kernel and automatically segments two hyperreflective retinal layers (inner plexiform layer (IPL) and outer plexiform layer (OPL)) in 50 optical coherence tomography (OCT) slabs and calculates the smoothness index (SI). The Bland-Altman plots, mean absolute error, root mean square error and signed error calculations revealed a modest discrepancy between the manual approach, used as the ground truth, and the corresponding automated segmentation of IPL/ OPL, as well as SI measurements in OCT slabs. It was concluded that the constructed algorithm may be employed as a reliable, rapid and convenient approach for segmenting IPL/OPL and calculating SI in the appropriate layers.