Comparative analysis of retinal microvascular parameters in healthy individuals with or without carotid artery stenosis or plaque.

PURPOSE: To evaluate the correlations between retinal microvascular changes and carotid artery stenosis (CAS) with and without plaques using fundus photography. METHODS: Patients who had undergone bilateral carotid ultrasonography and bilateral fundus photography were divided into the following groups based on the carotid intima-media thickness (IMT) determined via ultrasonography in this retrospective, observational study: the control and CAS (comprising CAS with and without plaque subgroups) groups. The following retinal indicators were determined via fundus photography based on a deep learning algorithm: the arteriole-to-venule ratio (AVR), whole retinal fractal dimension (FD), arteriolar fractal dimension (AFD), venular fractal dimension (VFD), vascular density (VD), and VD within 3 mm (VD3mm) and 5 mm (VD5mm) from the macular fovea. The correlations between these indicators and IMT were also assessed. RESULTS: In total, 715 participants, comprising 313 participants with CAS (CAS group; 91 with plaque and 222 without plaque) and 402 participants without CAS (control group), participated in this study. AFD, VFD, and FD in the CAS group were significantly lower than those in the control group (all p < 0.001). VD, VD3mm, and VD5mm showed significant differences between the groups (all p < 0.05). VFD in the CAS with plaque group was lower than that in the group without plaque (p = 0.014). VD3mm, and VD5mm showed significantly negative correlations with IMTmin in the CAS subgroup. CONCLUSIONS: AFD, VFD, FD, VD, VD3 mm, and VD5 mm decreased, and fundus photography based on deep learning algorithm may provide new approaches for screening of CAS.

The influence of insulin on diabetic retinopathy and retinal vessel parameters in diabetes.

AIM: To investigate the associations between insulin use and diabetic retinopathy (DR), and retinal vascular parameters in type 2 diabetes (T2DM). METHODS: A total of 6,374 T2DM patients, consisting of 2,231 patients receiving insulin alone and 4143 patients without any hypoglycemic medication, were included in cross-sectional analyses. Among those without DR at baseline, 791 patients were followed for three years in longitudinal analyses. Fundus photography was taken to diagnose DR and calculate central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), arteriolar-to-venular ratio (AVR), and vascular tortuosity. Inverse probability treatment-weighted analyses were performed. RESULTS: After adjusting for gender, age, body mass index, blood pressure, blood glucose, T2DM duration, smoking, and alcohol use, insulin users showed a higher risk of DR (odds ratio (OR) = 2.27, 95% confidence interval (95%CI) = 2.08-2.48, P < 0.001), larger CRVE (ß = 3.92, 95%CI = 2.46-5.37, P < 0.001), smaller AVR (ß=-0.0083, 95%CI=-0.0121- -0.0046, P < 0.001), and larger vascular curvature (ß = 0.19, 95%CI = 0.05-0.33, P = 0.008). After 3 years, insulin users had a higher risk of developing DR (OR = 1.94; 95% CI = 1.37-2.73, P = 0.002), and greater change in CRVE (ß = 3.92, 95%CI = 0.96-6.88, P = 0.009). CONCLUSIONS: The impact of insulin on the retinal microvasculature provides support for linking insulin to the increased risk of DR, as well as cardiovascular events in T2DM.

Deep Learning-Enabled Vasculometry Depicts Phased Lesion Patterns in High Myopia Progression.

PURPOSE: To investigate the potential phases in myopic retinal vascular alterations for further elucidating the mechanisms underlying the progression of high myopia (HM). METHODS: For this retrospective study, participants diagnosed with high myopia at Beijing Tongren Hospital were recruited. Based on bionic mechanisms of human vision, an intelligent image processing model was developed and utilized to extract and quantify the morphological characteristics of retinal vasculatures in different regions measured by papilla-diameter (PD), including vascular caliber, arteriole-to-venule ratio (AVR), tortuosity, the angle of the vascular arch (AVA), the distance of the vascular arch (DVA), density, fractal dimension, and venular length. In addition, the optic disc and the area of peripapillary atrophy (PPA) were also quantified. The characteristics of the overall population, as well as patients aged less than 25 years old, were compared by different genders. Univariate and multiple linear regression analyses were conducted to investigate the correlation of retinal vasculature parameters with PPA width, and detailed trends of the vascular indicators were analyzed to explore the potential existence of staged morphological changes. FINDINGS: The study included 14,066 fundus photographs of 5775 patients (aged 41.2 ± 18.6 years), of whom 7379 (61.2 %) were female. The study included 12,067 fundus photographs of 5320 patients (aged 41.2 ± 18.6 years). Significant variations in the morphological parameters of retinal vessels were observed between males and females. After adjusting for age and sex, multiple linear regression analysis showed that an increased PPA width ratio was associated with lower AVA (1PD), DVA (1PD), vascular caliber (0.5-1.0 PD), tortuosity (0.5-1.0 PD), density and fractal dimension (all P < 0.001, Spearman's ρ < 0). Overall, the changes in retinal vascular morphology showed two phases: tortuosity (0.5-1.0PD) and AVA (1PD) decreased rapidly in the first stage but significantly more slowly in the second stage, while vascular density and fractal dimension showed a completely opposite trend with an initial slow decline followed by a rapid decrease. CONCLUSIONS: This study identified two distinct phases of retinal vascular morphological changes during the progression of HM. Traction lesions were predominant in the initial stage, while atrophic lesions were predominant in the later stage. These findings provide further insight into the development mechanism of HM from the perspective of retinal vasculature.

Prediction model for asymptomatic carotid atherosclerosis using retinal microvascular intelligent analysis: A retrospective study.

IMPORTANCE: Early detection and timely diagnosis of asymptomatic carotid atherosclerosis significantly assist in the prevention of ischemic stroke for them. OBJECTIVE: This observational study aimed to develop and validate a novel prediction model to assist in the early diagnosis of carotid atherosclerosis based on new characteristic variables screened by retinal microvascular intelligence analysis. MAIN OUTCOME(S) AND METHOD (S): The least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the prediction model. Receiver operating characteristic (ROC) curves and area under the curve (AUC), calibration plots and brier score (BS), and decision curve analysis (DCA) were used to evaluate the risk model's discrimination, calibration, and clinical applicability. RESULTS: Age, gender, diabetes mellitus (DM), drinking history, vascular branching angle, mean vascular diameter within 0.5-1.0 papillary diameter (PD), curvature tortuosity arteriole in the inferior region of the optic disc, and vascular density in the nasal region of the optic disc were identified as characteristic variables for carotid atherosclerosis with retinal microvascular intelligence analysis. The predictive nomogram model presented good discrimination with AUCs of 0.790 (0.774-0.806), and the calibration curve displayed high consistency between predicted and actual probability. The DCA demonstrated that this nomogram model led to net benefits in a threshold probability range of 20 %-94 % and could be adapted for clinical decision-making. The results of the 100-bootstrap resampling strategy for internal validation also show that the risk model is well discriminated with an AUC of 0.789 and excellent calibration. External validation showed good discrimination with AUCs of 0.703 (0.627 - 0.779) and good calibration, the risk threshold is 10 %-92 % in terms of DCA. CONCLUSIONS AND RELEVANCE: The novel prediction model based on retinal microvascular intelligence analysis constructed in this study could be effective prognoses for predicting the risk of asymptomatic carotid atherosclerosis in a Chinese screening population.

Remnant cholesterol is correlated with retinal vascular morphology and diabetic retinopathy in type 2 diabetes mellitus: a cross-sectional study.

BACKGROUND: The association between remnant cholesterol (RC) and diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) remains unclear. Morphological changes in retinal vessels have been reported to predict vascular complications of diabetes, including DR. METHODS: This cross-sectional study included 6535 individuals with T2DM. The RC value was calculated using the recognized formula. The retinal vascular parameters were measured using fundus photography. The independent relationship between RC and DR was analyzed using binary logistic regression models. Multiple linear regression and subgroup analyses were employed to investigate the link between RC and vascular parameters, including the retinal arteriolar diameter (CRAE), venular diameter (CRVE), and fractal dimension (Df). Mediation analysis was performed to assess whether the vascular morphology could explain the association between RC and DR. RESULTS: RC was independently associated with DR in patients with a longer duration of T2DM (> 7 years). Patients with the highest quartile RC levels had larger CRAE (5.559 [4.093, 7.025] µm), CRVE (7.620 [5.298, 9.941] µm) and Df (0.013 [0.009, 0.017]) compared with patients with the lowest quartile RC levels. Results were robust across different subgroups. The association between RC and DR was mediated by CRVE (0.020 ± 0.005; 95% confidence interval: 0.012-0.032). CONCLUSIONS: RC may be a risk factor for DR among those who have had T2DM for a longer period of time. Higher RC levels were correlated with wider retinal arterioles and venules as well as higher Df, and it may contribute to DR through the dilation of retinal venules.

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.

Artificial Intelligence-based quantitative evaluation of retinal vascular parameters in thyroid-associated ophthalmopathy.

PURPOSE: Thyroid-associated ophthalmopathy (TAO) may result in increased metabolism and abnormalities in microcirculation. The fractal dimension (Df) of retinal vessels has been shown to be related to the pathology of a number of ophthalmic disorders, but it hasn't been investigated in TAO. METHODS: We analyzed 1078 participants aged 18 to 72 (548 healthy volunteers and 530 TAO). Images were captured using a non-mydriatic 45-degree fundus camera. Baseline retinal characteristics, such as vessel width, tortuosity, and Df were measured using semiautomated software from fundus images. The average retinal parameters were compared between the two groups. The receiver operation curve (ROC) was used to assess the diagnostic efficacy of various retinal vascular parameters for TAO. RESULTS: Despite controlling for potential confounding variables, Df, vessel width, and tortuosity significantly increased in TAO compared to healthy volunteers. Compared to active TAO, patients in the inactive phase had a larger retinal venous caliber (p < 0.05), but there was no difference in Df or arterial caliber. Moderate and severe cases had a higher Df compared with mild cases (EUGOGO guidelines). The area under the ROC for Df, tortuosity, and vascular caliber in the diagnosis of TAO was 0.904 (95% CI: 0.884-0.924), 0.638 (95% CI: 0.598-0.679), and 0.617 (95% CI: 0.576-0.658), respectively. CONCLUSIONS: Due to its accessibility, affordability, and non-invasive nature, retinal vascular Df may serve as a surrogate marker for TAO and might be used to identify severe cases. With relatively high diagnostic performance, the Df is of some utility for the detection of TAO.

Distinctive Imaging Characteristics of Retinal and Cerebral Vessels between Central and Branch Retinal Vein Occlusion by MRI and AI-Based Image Analyzer.

Retinal vessels have been good predictive and prognostic imaging biomarkers for systemic or eye diseases. Numerous studies have shown that the two retinal vein occlusion entities may correlate with cardiovascular and cerebrovascular events or primary open-angle glaucoma. This study aims to investigate if there is a disparity in the correlations between branch RVO (BRVO) and central RVO (CRVO) with systemic disorders or POAG, thus explaining the pathogenic difference between BRVO and CRVO. This retrospective case-control study enrolled 59 RVO subjects (118 eyes), including 25 CRVO and 34 BRVO subjects, who received routine eye and brain MRI examinations. The geometric characteristics of the caliber of the retinal and cerebral blood vessels and the optic nerve subarachnoid space width (ONSASW) were measured. Multivariable logistic regression analysis showed that ONSASW at 3 mm behind the globe (p = 0.044) and the relative retinal venular calibers (p = 0.031) were independent risk factors for the CRVO-affected eyes group in comparison with the BRVO-affected eyes group after adjusting for age, duration of hypertension, BMI, and IOP. In the CRVO-affected eyes, narrower relative retinal arteriolar calibers (p = 0.041) and wider relative venular calibers (p = 0.011) were independent risk factors compared with the CRVO-contralateral normal eyes when adjusting for IOP. We concluded that BRVO may be more associated with cerebrovascular diseases, and CRVO may be correlated with primary angle glaucoma. The geometric characteristics difference between the retinal and cerebrovascular may explain the pathological difference between CRVO and BRVO.

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.

Quantization of Optic Disc Characteristics in Young Adults Based on Artificial Intelligence.

PURPOSE: This study aimed to automatically and quantitatively analyse the characteristics of the optic disc by applying artificial intelligence (AI) to fundus images. METHODS: A total of 1084 undergraduates were recruited in this cross-sectional study. The optic disc area, cup-to-disc ratio (C/D), optic disc tilt, and the area, width, and height of peripapillary atrophy (PPA) were automatically and quantitatively detected using AI. Based on axial length (AL), participants were divided into five groups: Group 1 (AL ≤ 23 mm); Group 2 (23 mm < AL≤ 24 mm); Group 3 (24 mm < AL≤ 25 mm); Group 4 (25 mm < AL< 26 mm) and Group 5 (AL ≥ 26 mm). Relationships between ocular parameters and optic disc characteristics were analysed. RESULT: A total of 999 undergraduates were included in the analysis. The prevalence of optic disc tilting and PPA were 47.1% and 92.5%, respectively, and increased with the severity of myopia. The mean optic disc area, PPA area, C/D, and optic disc tilt ratio were 1.97 ± 0.46 mm2, 0.84 ± 0.59 mm2, 0.18 ± 0.07, and 0.81 ± 0.08, respectively. In Group 5, the average optic disc area (1.84 ± 0.41 mm2) and optic disc tilt ratio (0.79 ± 0.08) were significantly smaller and the PPA area (1.12 ± 0.61 mm2) was significantly larger than those in the other groups. AL was negatively correlated with optic disc area and optic disc tilt ratio (r=-0.271, -0.219; both p < 0.001) and positively correlated with PPA area, width, and height (r = 0.421, 0.426, 0.345; all p < 0.01). A greater AL (ß = 0.284, p < 0.01) and a smaller optic disc tilt ratio (ß=-0.516, p < 0.01) were related to a larger PPA area. CONCLUSION: The characteristics of the optic disc can be feasibly and efficiently extracted using AI. The quantization of the optic disc might provide new indicators for clinicians to evaluate the degree of myopia.

Prevalence of Fundus Tessellation and Its Screening Based on Artificial Intelligence in Chinese Children: the Nanjing Eye Study.

INTRODUCTION: To investigate the prevalence of fundus tessellation (FT), and the threshold for screening FT using an artificial intelligence (AI) technology in Chinese children. METHODS: The Nanjing Eye Study was a population-based cohort study conducted in children born between September 2011 and August 2012 in Yuhuatai District of Nanjing. The data presented in this paper were obtained in 2019, when these children were 7 years old and underwent 45° non-mydriatic fundus photography. FT in whole fundus, macular area, and peripapillary area was manually recognized from fundus photographs and classified into three grades. Fundus tessellation density (FTD) in these areas was obtained by calculating the average exposed choroid area per unit area using artificial intelligence (AI) technology based on fundus photographs. The threshold for screening FT using FTD was determined using receiver operating characteristic (ROC) curve analysis. RESULTS: Among 1062 enrolled children (mean [± standard deviation] spherical equivalent: - 0.28 ± 0.70 D), the prevalence of FT was 42.18% in the whole fundus (grade 1: 36.53%; grade 2: 5.08%; grade 3: 0.56%), 45.57% in macular area (grade 1: 43.5%; grade 2: 1.60%; grade 3: 0.50%), and 49.72% in peripapillary area (grade 1: 44.44%; grade 2: 4.43%; grade 3: 0.85%), respectively. The threshold value of FTD for screening severe FT (grade ≥ 2) was 0.049 (area under curve [AUC] 0.985; sensitivity 98.3%; specificity 92.3%) in the whole fundus, 0.069 (AUC 0.987; sensitivity 95.5%; specificity 96.2%) in the macular area, and 0.094 (AUC 0.980; sensitivity 94.6%; specificity 94.2%) in the peripapillary area, respectively. CONCLUSION: Fundus tessellation affected approximately 40 in 100 children aged 7 years in China, indicating the importance and necessity of early FT screening. The threshold values of FTD provided by this study had high accuracy for detecting severe FT and might be applied for rapid screening.

Fundus Tessellated Density Assessed by Deep Learning in Primary School Children.

Purpose: To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning. Methods: Comprehensive ocular examinations were conducted in 577 children aged 7 years old from a population-based cross-sectional study, including biometric measurement, refraction, optical coherence tomography angiography, and 45° nonmydriatic fundus photography. FTD was defined as the average exposed choroid area per unit area of the fundus, and obtained by artificial intelligence technology. The distribution of FT was classified into the macular pattern and the peripapillary pattern according to FTD. Results: The mean FTD was 0.024 ± 0.026 in whole fundus. Multivariate regression analysis showed that greater FTD was significantly correlated with thinner subfoveal choroidal thickness, larger parapapillary atrophy, greater vessel density inside the optic disc, larger vertical diameter of optic disc, thinner retinal nerve fiber layer, and longer distance from optic disc center to macular fovea (all P < 0.05). The peripapillary distributed group had larger parapapillary atrophy (0.052 ± 0.119 vs 0.031 ± 0.072), greater FTD (0.029 ± 0.028 vs 0.015 ± 0.018), thinner subfoveal choroidal thickness (297.66 ± 60.61 vs 315.33 ± 66.46), and thinner retinal thickness (285.55 ± 10.89 vs 288.03 ± 10.31) than the macular distributed group (all P < 0.05). Conclusions: FTD can be applied as a quantitative biomarker to estimate subfoveal choroidal thickness in children. The role of blood flow inside optic disc in FT progression needs further investigation. The distribution of FT and the peripapillary pattern correlated more with myopia-related fundus changes than the macular pattern. Translational Relevance: Artificial intelligence can evaluate FT quantitatively in children, and has potential value for assisting in myopia prevention and control.

Optic disc and retinal vascular features in first 6 years of Chinese children.

Purpose: Retinal microvasculature plays an important role in children's fundus lesions and even in their later life. However, little was known on the features of normal retina in early life. The purpose of this study was to explore the normal retinal features in the first 6 years of life and provide information for future research. Methods: Children, aged from birth to 6 years old and diagnosed with various unilateral ocular diseases were included. Venous phase fundus fluorescein angiography images with the optic disc at the center were collected. Based on the ResUNet convolutional neural network, optic disc and retinal vascular features in the posterior retina were computed automatically. Results: A total of 146 normal eyes of 146 children were included. Among different age groups, no changes were shown in the optic disc diameter (y = -0.00002x + 1.362, R2 = 0.025, p = 0.058). Retinal vessel density and fractal dimension are linearly and strongly correlated (r = 0.979, p < 0.001). Older children had smaller value of fractal dimension (y = -0.000026x + 1.549, R2 = 0.075, p = 0.001) and narrower vascular caliber if they were less than 3 years old (y = -0.008x + 84.861, R2 = 0.205, p < 0.001). No differences were in the density (y = -0.000007x + 0.134, R2 = 0.023, p = 0.067) and the curvature of retinal vessels (lnC = -0.00001x - 4.657, R2 = 0.001, p = 0.667). Conclusions: Age and gender did not impact the optic disc diameter, vessel density, and vessel curvature significantly in this group of children. Trends of decreased vessel caliber in the first 3 years of life and decreased vessel complexity with age were observed. The structural characteristics provide information for future research to better understand the developmental origin of the healthy and diseased retina.

Application of Artificial Intelligence to Quantitative Assessment of Fundus Tessellated Density in Young Adults with Different Refractions.

INTRODUCTION: The aim of this study was to quantitatively assess fundus tessellated density (FTD) and associated factors by artificial intelligence (AI) in young adults. METHODS: A total of 1,084 undergraduates (age, 17-23 years old) were enrolled in November 2021. The students were divided into three groups according to axial length (AL): group 1 (AL <24.0 mm, n = 155), group 2 (24 mm ≤ AL <26 mm, n = 578), and group 3 (AL ≥26 mm, n = 269). FTD was calculated by extracting the fundus tessellations as the regions of interest (circle 1, diameter of 3.0 mm; circle 2, diameter of 6.0 mm) and then calculating the average exposed choroid area per unit area of fundus. RESULTS: Among 1,084 students, 1,002 (92.5%) students' FTDs were extracted. The mean FTD was 0.06 ± 0.06 (range, 0-0.40). In multivariate analysis, FTD was significantly associated with male sex, longer AL, thinner subfoveal choroid thickness (SFCT), increased choriocapillaris vessel density (VD), and decreased deeper choroidal VD (all p < 0.05). In circle 1 (diameter of 3.0 mm) and circle 2 (diameter of 6.0 mm), analysis of variance showed that the FTD of the nasal region (p < 0.05) was significantly larger than that of the superior, inferior, and temporal regions. CONCLUSION: AI-based imaging processing could improve the accuracy of fundus tessellation diagnosis. FTD was significantly associated with a longer AL, thinner SFCT, increased choriocapillaris VD, and decreased deeper choroidal VD.

Development and validation of a predictive risk model based on retinal geometry for an early assessment of diabetic retinopathy.

Aims: This study aimed to develop and validate a risk nomogram prediction model based on the retinal geometry of diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM) and to investigate its clinical application value. Methods: In this study, we collected the clinical data of 410 patients with T2DM in the Second Affiliated Hospital of Chongqing Medical University between October 2020 and March 2022. Firstly, the patients were randomly divided into a development cohort and a validation cohort in a ratio of 7:3. Then, the modeling factors were selected using the least absolute shrinkage and selection operator (LASSO). Subsequently, a nomogram prediction model was built with these identified risk factors. Two other models were constructed with only retinal vascular traits or only clinical traits to confirm the performance advantage of this nomogram model. Finally, the model performances were assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis (DCA). Results: Five predictive variables for DR among patients with T2DM were selected by LASSO regression from 33 variables, including fractal dimension, arterial tortuosity, venular caliber, duration of diabetes mellitus (DM), and insulin dosage (P< 0.05). A predictive nomogram model based on these selected clinical and retinal vascular factors presented good discrimination with an AUC of 0.909 in the training cohort and 0.876 in the validation cohort. By comparing the models, the retinal vascular parameters were proven to have a predictive value and could improve diagnostic sensitivity and specificity when combined with clinical characteristics. The calibration curve displayed high consistency between predicted and actual probability in both training and validation cohorts. The DCA demonstrated that this nomogram model led to net benefits in a wide range of threshold probability and could be adapted for clinical decision-making. Conclusion: This study presented a predictive nomogram that might facilitate the risk stratification and early detection of DR among patients with T2DM.

A Generic Pixel Pitch Calibration Method for Fundus Camera via Automated ROI Extraction.

Pixel pitch calibration is an essential step to make the fundus structures in the fundus image quantitatively measurable, which is important for the diagnosis and treatment of many diseases, e.g., diabetes, arteriosclerosis, hereditary optic atrophy, etc. The conventional calibration approaches require the specific parameters of the fundus camera or several specially shot images of the chess board, but these are generally not accessible, and the calibration results cannot be generalized to other cameras. Based on automated ROI (region of interest) and optic disc detection, the diameter ratio of ROI and optic disc (ROI-disc ratio) is quantitatively analyzed for a large number of fundus images. With the prior knowledge of the average diameter of an optic disc in fundus, the pixel pitch can be statistically estimated from a large number of fundus images captured by a specific camera without the availability of chess board images or detailed specifics of the fundus camera. Furthermore, for fundus cameras of FOV (fixed field-of-view), the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel. The average ROI-disc ratio is approximately constant, i.e., 6.404 ± 0.619 in the pixel, according to 40,600 fundus images, captured by different cameras, of 45° FOV. In consequence, the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel, and results show the pixel pitches of Canon CR2, Topcon NW400, Zeiss Visucam 200, and Newvision RetiCam 3100 cameras are 6.825 ± 0.666 µm, 6.625 ± 0.647 µm, 5.793 ± 0.565 µm, and 5.884 ± 0.574 µm, respectively. Compared with the manually measured pixel pitches, based on the method of ISO 10940:2009, i.e., 6.897 µm, 6.807 µm, 5.693 µm, and 6.050 µm, respectively, the bias of the proposed method is less than 5%. Since our method doesn't require chess board images or detailed specifics, the fundus structures on the fundus image can be measured accurately, according to the pixel pitch obtained by this method, without knowing the type and parameters of the camera.

Prediction of the Fundus Tessellation Severity With Machine Learning Methods.

Purpose: To predict the fundus tessellation (FT) severity with machine learning methods. Methods: A population-based cross-sectional study with 3,468 individuals (mean age of 64.6 ± 9.8 years) based on Beijing Eye Study 2011. Participants underwent detailed ophthalmic examinations including fundus images. Five machine learning methods including ordinal logistic regression, ordinal probit regression, ordinal log-gamma regression, ordinal forest and neural network were used. Main Outcome Measure: FT precision, recall, F1-score, weighted-average F1-score and AUC value. Results: Observed from the in-sample fitting performance, the optimal model was ordinal forest, which had correct classification rate (precision) of 81.28%, while 34.75, 93.73, 70.03, and 24.82% in each classified group by FT severity. The AUC value was 0.7249. And the F1-score was 65.05%, weighted-average F1-score was 79.64% on the whole dataset. For out-of-sample prediction performance, the optimal model was ordinal logistic regression, which had precision of 77.12% on the validation dataset, while 19.57, 92.68, 64.74, and 6.76% in each classified group by FT severity. The AUC value was 0.7187. The classification accuracy of light FT group was the highest, while that of severe FT group was the lowest. And the F1-score was 54.46%, weighted-average F1-score was 74.19% on the whole dataset. Conclusions: The ordinal forest and ordinal logistic regression model had the strong prediction in-sample and out-sample performance, respectively. The threshold ranges of the ordinal forest model for no FT and light, moderate, severe FT were [0, 0.3078], [0.3078, 0.3347], [0.3347, 0.4048], [0.4048, 1], respectively. Likewise, the threshold ranges of ordinal logistic regression model were ≤ 3.7389, [3.7389, 10.5053], [10.5053, 13.9323], > 13.9323. These results can be applied to guide clinical fundus disease screening and FT severity assessment.

The diagnostic accuracy of an intelligent and automated fundus disease image assessment system with lesion quantitative function (SmartEye) in diabetic patients.

BACKGROUND: With the diabetes mellitus (DM) prevalence increasing annually, the human grading of retinal images to evaluate DR has posed a substantial burden worldwide. SmartEye is a recently developed fundus image processing and analysis system with lesion quantification function for DR screening. It is sensitive to the lesion area and can automatically identify the lesion position and size. We reported the diabetic retinopathy (DR) grading results of SmartEye versus ophthalmologists in analyzing images captured with non-mydriatic fundus cameras in community healthcare centers, as well as DR lesion quantitative analysis results on different disease stages. METHODS: This is a cross-sectional study. All the fundus images were collected from the Shanghai Diabetic Eye Study in Diabetics (SDES) program from Apr 2016 to Aug 2017. 19,904 fundus images were acquired from 6013 diabetic patients. The grading results of ophthalmologists and SmartEye are compared. Lesion quantification of several images at different DR stages is also presented. RESULTS: The sensitivity for diagnosing no DR, mild NPDR (non-proliferative diabetic retinopathy), moderate NPDR, severe NPDR, PDR (proliferative diabetic retinopathy) are 86.19, 83.18, 88.64, 89.59, and 85.02%. The specificity are 63.07, 70.96, 64.16, 70.38, and 74.79%, respectively. The AUC are PDR, 0.80 (0.79, 0.81); severe NPDR, 0.80 (0.79, 0.80); moderate NPDR, 0.77 (0.76, 0.77); and mild NPDR, 0.78 (0.77, 0.79). Lesion quantification results showed that the total hemorrhage area, maximum hemorrhage area, total exudation area, and maximum exudation area increase with DR severity. CONCLUSIONS: SmartEye has a high diagnostic accuracy in DR screening program using non-mydriatic fundus cameras. SmartEye quantitative analysis may be an innovative and promising method of DR diagnosis and grading.