Population-Based Artificial Intelligence Assessment of Relationship Between the Risk Factors for Diabetic Retinopathy in Indian Population.

PURPOSE: Risk factors (RFs), like 'body mass index (BMI),' 'age,' and 'gender' correlate with Diabetic Retinopathy (DR) diagnosis and have been widely studied. This study examines how these three secondary RFs independently affect the predictive capacity of primary RFs. METHODS: The dataset consisted of four population-based studies on the prevalence of DR and associated RFs in India between 2001 and 2010. An Autoencoder was employed to categorize RFs as primary or secondary. This study evaluated six primary RFs coupled independently with each secondary RF on five machine-learning models. RESULTS: The secondary RF 'gender' gave a maximum increase in Area under the curve (AUC) score to predict DR when combined separately with 'insulin treatment,' 'fasting plasma glucose,' 'hypertension history,' and 'glycosylated hemoglobin' with a maximum increase in AUC for the Naive Bayes model from 0.573 to 0.646, for the Support Vector Machines (SVM) model from 0.644 to 0.691, for the SVM model from 0.487 to 0.607, and for the Decision Tree model from 0.8 to 0.848, respectively. The secondary RFs 'age' and 'BMI' gave a maximum increase in AUC score to predict DR when combined separately with 'diabetes mellitus duration' and 'systolic blood pressure,' with a maximum increase in AUC for the SVM model from 0.389 to 0.621, and for the Decision Tree model from 0.617 to 0.713, respectively. CONCLUSION: The risk factor 'gender' was the best secondary RF in predicting DR compared to 'age' and 'BMI,' increasing the predictive power of four primary RFs.

Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India.

This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The study uses a t-test and Shapely additive explanations (SHAP) to rank the risk factors. Then, it uses five machine learning models (K-Nearest Neighbor, Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes) to identify the unimportant risk factors based on the area under the curve criterion to predict DR. To determine the overall significance of risk variables, a weighted average of each classifier's importance is used. The ranking of risk variables is provided to machine learning models. To construct a model for DR prediction, the combination of risk factors with the highest AUC is chosen. The results show that the risk factors glycosylated hemoglobin and systolic blood pressure were present in the top three risk factors for DR in all five machine learning models when the t-test was used for ranking. Furthermore, the risk factors, namely, systolic blood pressure and history of hypertension, were present in the top five risk factors for DR in all the machine learning models when SHAP was used for ranking. Finally, when an ensemble of the five machine learning models was employed, independently with both the t-test and SHAP, systolic blood pressure and diabetes mellitus duration were present in the top four risk factors for diabetic retinopathy. Decision Tree and K-Nearest Neighbor resulted in the highest AUCs of 0.79 (t-test) and 0.77 (SHAP). Moreover, K-Nearest Neighbor predicted DR with 82.6% (t-test) and 78.3% (SHAP) accuracy.

An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization.

Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.

Advances in the diagnosis of herpes simplex stromal necrotising keratitis: A feasibility study on deep learning approach.

Purpose: Infectious keratitis, especially viral keratitis (VK), in resource-limited settings, can be a challenge to diagnose and carries a high risk of misdiagnosis contributing to significant ocular morbidity. We aimed to: employ and study the application of artificial intelligence-based deep learning (DL) algorithms to diagnose VK. Methods: A single-center retrospective study was conducted in a tertiary care center from January 2017 to December 2019 employing DL algorithm to diagnose VK from slit-lamp (SL) photographs. Three hundred and seven diffusely illuminated SL photographs from 285 eyes with polymerase chain reaction-proven herpes simplex viral stromal necrotizing keratitis (HSVNK) and culture-proven nonviral keratitis (NVK) were included. Patients having only HSV epithelial dendrites, endothelitis, mixed infection, and those with no SL photographs were excluded. DenseNet is a convolutional neural network, and the two main image datasets were divided into two subsets, one for training and the other for testing the algorithm. The performance of DenseNet was also compared with ResNet and Inception. Sensitivity, specificity, receiver operating characteristic (ROC) curve, and the area under the curve (AUC) were calculated. Results: The accuracy of DenseNet on the test dataset was 72%, and it performed better than ResNet and Inception in the given task. The AUC for HSVNK was 0.73 with a sensitivity of 69.6% and specificity of 76.5%. The results were also validated using gradient-weighted class activation mapping (Grad-CAM), which successfully visualized the regions of input, which are significant for accurate predictions from these DL-based models. Conclusion: DL algorithm can be a positive aid to diagnose VK, especially in primary care centers where appropriate laboratory facilities or expert manpower are not available.

The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review.

In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.

Relationship of fractal analysis in retinal microvascularity with demographic and diagnostic parameters.

Problems and diseases with eye are common in diabetic patients. Early diagnosis and detection of various diseases like retinopathy, neuropathy and nephropathy is crucial in diabetic patients. Certain demographic and diagnostic parameters play a significant role in predicting diseases related to diabetes. Development of a novel diagnostic method which helps to predict the disease by establishing a significant correlation with the demographic and diagnostic parameters is of prime importance. This study proposes a new methodology in which retinal fractals are obtained for the images and the derived retinal fractals are analysed to aid in disease prediction. This study comprises of images from patients with retinopathy, non retinopathy, neuropathy, nephropathy and hypertension. The proposed research is carried out in two aspects: 1) to correlate the retinal fractals of retinopathy and non retinopathy images with certain demographic and diagnostic parameters and interpret its significance, and 2) to exhibit a relationship between the retinal fractals and various diseases/addictive habit to facilitate the prediction of the disease/addictive habit. Hausdorff fractal dimension (HFD) was applied and higher fractal dimension was obtained for healthy cases. Then using Statistical Package for the Social Sciences (SPSS) various statistical parameters and significance were calculated to analyse the relationship. Analysis results showed that fractal value helped in distinguishing between the retinopathy and non retinopathy conditions. It also helped in diagnosing the presence and absence of hypertension. Correlation analysis between certain demographic parameters and fractal value showed a positive correlation whereas few exhibited negative correlation.

Comparison of Two Ultra-Widefield Cameras With High Image Resolution and Wider View for Identifying Diabetic Retinopathy Lesions.

Purpose: To compare the effectiveness of the Optos P200dTx and Zeiss Clarus 500 fundus cameras in detecting diabetic retinopathy (DR) lesions. Methods: A cross-sectional study was conducted among 243 patients with clinically diagnosed diabetes mellitus who were referred for an eye examination from two tertiary eye care centers in Chennai, India. Patients underwent DR screening based on mydriatic fundal images acquired by both fundal cameras. Fundal images from the two separate devices for each eye were compared based on accurately identified pathological retinal lesions with respect to type and location. Results: When studying lesions of the central retina, they were better identified by the Zeiss Clarus compared with the Optos P200dTx, with six out of eight being statistically significant (P < 0.05). However, lesions of the mid-peripheral retina and peripheral retina were better identified by the Optos P200dTx than the Zeiss Clarus, with three out of eight lesions and five out of eight lesions being statistically significant (P < 0.05), respectively. Based on the color and size of lesions, the Optos P200dTx had a higher chance (59.6%) of missing white lesions than did the Zeiss Clarus (17%) (P < 0.0001). Consequently, small- and medium-sized lesions were missed more by the Optos P200dTx (30.72% and 32.63%, respectively) than the Zeiss Clarus (22.3% and 19.30%, respectively). Conclusions: The capability of detecting or missing a particular DR lesion among diabetics differed between the two cameras based on effective field of view, resolution, and the retinal zone being imaged. Translational Relevance: The choice of which ultra-widefield camera to be used for screening DR can be based on the greater prevalence of central versus peripheral retinal lesions noted in the patient population seen in a clinical practice.

Canonical and atypical E2Fs regulate the mammalian endocycle.

The endocycle is a variant cell cycle consisting of successive DNA synthesis and gap phases that yield highly polyploid cells. Although essential for metazoan development, relatively little is known about its control or physiologic role in mammals. Using lineage-specific cre mice we identified two opposing arms of the E2F program, one driven by canonical transcription activation (E2F1, E2F2 and E2F3) and the other by atypical repression (E2F7 and E2F8), that converge on the regulation of endocycles in vivo. Ablation of canonical activators in the two endocycling tissues of mammals, trophoblast giant cells in the placenta and hepatocytes in the liver, augmented genome ploidy, whereas ablation of atypical repressors diminished ploidy. These two antagonistic arms coordinate the expression of a unique G2/M transcriptional program that is critical for mitosis, karyokinesis and cytokinesis. These results provide in vivo evidence for a direct role of E2F family members in regulating non-traditional cell cycles in mammals.