Alauddin Bhuiyan

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. Early detection is essential for implementing preventative measures that can slow or stop the progression of late AMD. This study evaluates the performance of an AI-based system designed to detect referable AMD, defined as more than early AMD (mteAMD), in adults over 50 who have not been previously diagnosed. Using color fundus photographs, we recruited 845 subjects from three primary care and three general ophthalmology clinics in New York City. Non-dilated images of both eyes were taken, and for validation, dilated images were reviewed by three ophthalmologists who classified the cases as no AMD, early, intermediate, or late AMD. The system’s performance was assessed on both a per-patient and per-eye basis, comparing its results to expert gradings using metrics such as AUC, sensitivity, specificity, positive predictive value, and negative predictive value. For identifying mteAMD at the subject level, the AI system achieved an AUC of 0.92, with a sensitivity of 90.27% and specificity of 83.36%, demonstrating its strong potential for early diagnosis and screening of AMD in real-world clinical settings.

Stroke is a leading cause of death and disability in developed countries. We validated an AI-based prediction model for incident stroke using sensors such as fundus cameras and ophthalmoscopes for retinal images, along with socio-demographic data and traditional risk factors. The model was trained on a proprietary dataset of over 6500 participants, including 171 with 5-year incident strokes and 242 with 10-year incident strokes. The model provides separate 5-year and 10-year risk scores. The model was externally validated on the UK Biobank dataset (3000 subjects with 5-year incident strokes). Using retinal imaging, our models identified individuals with 5-year incident strokes with 80% sensitivity, 82% specificity, and an AUC of 0.83, and predicted 10-year incidents with 72% sensitivity, 78% specificity, and an AUC of 0.79. In comparison, for the 10-year model, the AUC for the Framingham score was 0.73, and the CHADS2 score was 0.74. On the Biobank external dataset, our 5-year model (without retinal features) demonstrated moderate but lower sensitivity (69.3%) and specificity (66.4%) compared to its performance on the proprietary dataset (with retinal features). Using a multi-ethnic dataset, we developed and validated a prediction model that improves stroke risk identification for 5-year and 10-year incidences by incorporating retinal features.

A novel automated method for segmenting retinal layers in three‐dimensional (3D) space from spectral domain optical coherence tomography (SD‐OCT) images. Compared to 2D segmentation, 3D segmentation uses more data and produces findings that are more accurate and reliable. The class‐specific area of interest (ROI) choice and three important reference class approximations make the suggested technique precise, effective, and reliable. In the first step, contours are detected based on gradient intensity. To choose a smaller region of interest (ROI), the second stage entails acquiring the identified boundary neighbour B scan data for the selected ROI by categorising the problem as a graph problem. The third stage involves locating edge pixels using Canny Edge Detection from nodes. In order to calculate the edge weight of a histogram, slope similarity to the reference line and node characteristics are considered. The fourth phase boundary is precisely found by Dijkstra's shortest path algorithm. The accuracy of the method was tested based on 288 B scans of 12 patients (ten normal macular degeneration (AMD) subjects and 2 age‐related subjects from two different institutions). Five recent automated procedures are compared with the results to further validate the findings of the fifth phase. The outcomes demonstrate a mean original mean square error (RMSE) for each of the cut‐off values, which are 2.82, 4.88, 2.03, 3.77, and 0.64 pixels, respectively. As can be seen, the suggested strategy outperforms the existing models' significantly with a return on investment of 0.26 pixels.

Background and Purpose: Cardiovascular disease (CVD) is a leading cause of death and disability in the world. Many CVD risk prediction models have been created, but those most widely used in clinical settings have not been externally validated, a significant gap addressed herein. Methods: Using the Multi-Ethnic Study of Atherosclerosis (MESA), we have externally validated the Framingham Risk Score, ASSIGN (Assessing the cardiovascular disease risk using SIGN) risk score, Atherosclerotic Cardiovascular Disease (ASCVD) risk score, and the European SCORE model, which were selected based on popularity among clinicians and frequency of clinical use. The models were implemented in a computer program based on published algorithms, and 100 incident CVD and 100 non-incident CVD subjects from MESA were selected for testing the repeatability. The outcome in both models achieved 100% correlation. The individual model accuracy was tested by computing their sensitivity, specificity, accuracy, and C-statistics. Results: For discrimination, ASCVD showed the highest C-statistic (0.717) and SCORE the lowest (0.677). The Framingham score provided the most consistent results among the models, with a sensitivity of 69% and a specificity of 62%, but overestimated the risks as shown by the calibration results. SCORE was relatively inconsistent, with a sensitivity of 34% and specificity of 85%, for a risk threshold set at the top 20%. Conclusions: Calibration experiments showed that the transportability of the risk was generally poor in all models, with Framingham and SCORE particularly overestimating the risks for the American population There is clearly a need for better models to predict CVD risk in the multi-ethnic American population.

Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2–5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management.

Background: Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are among the leading causes of blindness in the United States and other developed countries. Early detection is the key to prevention and effective treatment. We have built an artificial intelligence-based screening system which utilizes a cloud-based platform for combined large scale screening through primary care settings for early diagnosis of these diseases. Methods: iHealthScreen Inc., an independent medical software company, has developed automated AMD and DR screening systems utilizing a telemedicine platform based on deep machine learning techniques. For both diseases, we prospectively imaged both eyes of 340 unselected non-dilated subjects over 50 years of age. For DR specifically, 152 diabetic patients at New York Eye and Ear faculty retina practices, ophthalmic and primary care clinics in New York city with color fundus cameras. Following the initial review of the images, 308 images with other confounding conditions like high myopia and vascular occlusion, and poor quality were excluded, leaving 676 eligible images for AMD and DR evaluation. Three ophthalmologists evaluated each of the images, and after adjudication, the patients were determined referrable or non-referable for AMD DR. Concerning AMD, 172 were labeled referable (intermediate or late), and 504 were non-referable (no or early). Concurrently, regarding DR, 33 were referable (moderate or worse), and 643 were non-referable (none or mild). All images were uploaded to iHealthScreen’s telemedicine platform and analyzed by the automated systems for both diseases. The system performances are tested on per eye basis with sensitivity, specificity, accuracy, and kappa scores with respect to the professional graders. Results: In identifying referable DR, the system achieved a sensitivity of 97.0% and a specificity of 96.3%, and a kappa score of 0.70 on this prospective dataset. For AMD, the sensitivity was 86.6%, the specificity of 92.1%, and a kappa score of 0.76. Conclusions: The AMD and DR screening tools achieved excellent performance operating together to identify two retinal diseases prospectively in mixed datasets, demonstrating the feasibility of such tools in the early diagnosis of eye diseases. These early screening tools will help create an even more comprehensive system capable of being trained on other retinal pathologies, a goal within reach for public health deployment.

Backgrounds & Objective Glaucomatous vision loss may be preceded by an enlargement of the cup-to-disc ratio (CDR). We propose to develop and validate an artificial intelligence based CDR grading system that may aid in effective glaucoma-suspect screening. Design, Setting & Participants 1546 disc-centered fundus images were selected including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images randomly selected from the Age-Related EyeDisease Study, and Singapore Malay Eye Study to develop the system. First, a proprietary semi-automated software was used by an expert grader to quantify vertical CDR. Then, using CDR below 0.5 (not suspect) and CDR above 0.5 (glaucoma-suspect), deep learning architectures were used to train and test a binary classifier system. Measurements The binary classifier, with glaucoma-suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC. Results The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; AUC, 0.93). For external validation, the Retinal Fundus Image database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; AUC, 0.85). Conclusions Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary care settings.

Background and Objective . Glaucomatous vision loss may be preceded by an enlargement of the cup‐to‐disc ratio (CDR). We propose to develop and validate an artificial‐intelligence‐based CDR grading system that may aid in effective glaucoma‐suspect screening. Design, Setting, and Participants . 1546 disc‐centered fundus images were selected, including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images were randomly selected from the Age‐Related Eye Disease Study and Singapore Malay Eye Study to develop the system. First, a proprietary semiautomated software was used by an expert grader to quantify vertical CDR. Then, using CDR below 0.5 (nonsuspect) and CDR above 0.5 (glaucoma suspect), deep‐learning architectures were used to train and test a binary classifier system. Measurements . The binary classifier, with glaucoma suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC. Results . The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85). Conclusions . Having demonstrated an accurate and fully automated glaucoma‐suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary‐care settings.