A Novel Grading System for Diffuse Chorioretinal Atrophy in Pathologic Myopia.

INTRODUCTION: This study aims to quantitatively assess diffuse chorioretinal atrophy (DCA) in pathologic myopia and establish a standardized classification system utilizing artificial intelligence. METHODS: A total of 202 patients underwent comprehensive examinations, and 338 eyes were included in the study. The methodology involved image preprocessing, sample labeling, employing deep learning segmentation models, measuring and calculating the area and density of DCA lesions. Lesion severity of DCA was graded using statistical methods, and grades were assigned to describe the morphology of corresponding fundus photographs. Hierarchical clustering was employed to categorize diffuse atrophy fundus into three groups based on the area and density of diffuse atrophy (G1, G2, G3), while high myopic fundus without diffuse atrophy was designated as G0. One-way analysis of variance (ANOVA) and nonparametric tests were conducted to assess the statistical association with different grades of DCA. RESULTS: On the basis of the area and density of DCA, the condition was classified into four grades: G0, G1 (0 < density ≤ 0.093), G2 (0.093 < density ≤ 0.245), and G3 (0.245 < density ≤ 0.712). Fundus photographs depicted a progressive enlargement of atrophic lesions, evolving from punctate-shaped to patchy with indistinct boundaries. DCA atrophy lesions exhibited a gradual shift in color from brown-yellow to yellow-white, originating from the temporal side of the optic disc and extending towards the macula, with severe cases exhibiting widespread distribution throughout the posterior pole. Patients with DCA were significantly older [34.00 (27.00, 48.00) vs 29.00 (26.00, 34.00) years], possessed a longer axial length (28.85 ± 1.57 vs 27.11 ± 1.01 mm), and exhibited a more myopic spherical equivalent [- 13.00 (- 16.00, - 10.50) vs - 9.09 ± 2.41 D] compared to those without DCA (G0) (all P < 0.001). In eyes with DCA, a trend emerged as grades increased from G1 to G3, showing associations with older age, longer axial length, deeper myopic spherical equivalent, larger area of parapapillary atrophy, and increased fundus tessellated density (all P < 0.001). CONCLUSIONS: The novel grading system for DCA, based on assessments of area and density, serves as a reliable measure for evaluating the severity of this condition, making it suitable for widespread application in the screening of pathologic myopia.

Fundus Tessellated Density of Pathologic Myopia.

PURPOSE: The study aimed to quantitatively evaluate the fundus tessellated density (FTD) in different categories of pathologic myopia (PM) using fundus photographs with the application of artificial intelligence. METHODS: A retrospective review of 407 PM (META-PM, Category 2-Category 4) eyes was conducted, employing a biomimetic mechanism of human vision and integrated image processing technologies for FTD extraction and calculation. Different regions of interest were analyzed, including circle O4.5 (optic disc centered, diameter of 4.5 mm) and circle M1.0, M3.0, M6.0 (macular centered, diameter of 1.0, 3.0, and 6.0 mm), using 2 partitioning methods ("X" and "+"). The density of patchy (Category 3) or macular atrophy (Category 4) areas was quantified. Univariate and multivariate linear regression analyses were performed to assess the association with FTD. RESULTS: The mean FTD of total PM eyes was 0.283, ranging from 0.002 to 0.500, and demonstrating a negative correlation with the PM category. In multivariate analysis, age was found to be significantly associated with FTD ( P <0.05), while axial length did not show a significant association. Fundus tessellation of circle O4.5 and circle M6.0 displayed associations with the FTD across different PM categories. The "X" partitioning method better fit the circle M6.0 region, while both methods were suitable for the circle O4.5 region. After excluding the patchy and macular atrophic areas, the mean FTD values were 0.346 in Category 2, 0.261 in Category 3, and 0.186 in Category 4. CONCLUSIONS: The study revealed a decreasing trend in FTD values across different categories of PM, regardless of the presence or absence of patchy or macular atrophic areas. Quantifying FTD in PM could be a valuable tool for improving the existing PM classification system and gaining insights into the origin of posterior staphyloma and visual field defects in high myopia.

Relationships between quantitative retinal microvascular characteristics and cognitive function based on automated artificial intelligence measurements.

Introduction: The purpose of this study is to assess the relationship between retinal vascular characteristics and cognitive function using artificial intelligence techniques to obtain fully automated quantitative measurements of retinal vascular morphological parameters. Methods: A deep learning-based semantic segmentation network ResNet101-UNet was used to construct a vascular segmentation model for fully automated quantitative measurement of retinal vascular parameters on fundus photographs. Retinal photographs centered on the optic disc of 3107 participants (aged 50-93 years) from the Beijing Eye Study 2011, a population-based cross-sectional study, were analyzed. The main parameters included the retinal vascular branching angle, vascular fractal dimension, vascular diameter, vascular tortuosity, and vascular density. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Results: The results showed that the mean MMSE score was 26.34 ± 3.64 (median: 27; range: 2-30). Among the participants, 414 (13.3%) were classified as having cognitive impairment (MMSE score < 24), 296 (9.5%) were classified as mild cognitive impairment (MMSE: 19-23), 98 (3.2%) were classified as moderate cognitive impairment (MMSE: 10-18), and 20 (0.6%) were classified as severe cognitive impairment (MMSE < 10). Compared with the normal cognitive function group, the retinal venular average diameter was significantly larger (p = 0.013), and the retinal vascular fractal dimension and vascular density were significantly smaller (both p < 0.001) in the mild cognitive impairment group. The retinal arteriole-to-venular ratio (p = 0.003) and vascular fractal dimension (p = 0.033) were significantly decreased in the severe cognitive impairment group compared to the mild cognitive impairment group. In the multivariate analysis, better cognition (i.e., higher MMSE score) was significantly associated with higher retinal vascular fractal dimension (b = 0.134, p = 0.043) and higher retinal vascular density (b = 0.152, p = 0.023) after adjustment for age, best corrected visual acuity (BCVA) (logMAR) and education level. Discussion: In conclusion, our findings derived from an artificial intelligence-based fully automated retinal vascular parameter measurement method showed that several retinal vascular morphological parameters were correlated with cognitive impairment. The decrease in retinal vascular fractal dimension and decreased vascular density may serve as candidate biomarkers for early identification of cognitive impairment. The observed reduction in the retinal arteriole-to-venular ratio occurs in the late stages of cognitive impairment.