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Retinal imaging is crucial for observing the retina and accurately diagnosing pathological problems. Optical Coherence Tomography (OCT) has been a transformative breakthrough for developing high-resolution cross-sectional images. It is imperative to delineate the multiple layers of the retina for a proper diagnosis. A novel segmentation-based approach is introduced in this study to identify seven distinct layers of the retina using OCT images. The developed approach presents SliceOCTNet, a customized U-shaped Convolutional Neural Network (CNN) that introduces group normalization and intricate skip connections. Paired alongside a hybrid loss function, the SliceOCTNet outperformed most state-of-the-art approaches. The introduction of Group Normalization in SliceOCTNet stabilized the model and improved layer identification even when working with small datasets. The use of skip connections also contributed to an improvement in the spatial outlook of the model. Implementing a hybrid loss function addresses the class imbalance problem in the dataset. Duke University’s spectral-domain optical coherence tomography (SD-OCT) B-scan dataset of healthy and Diabetic Macular Edema (DME) afflicted patients was utilized to train and evaluate the SliceOCTNet. The model accurately recognizes the seven layers of the retina. It can achieve a high dice coefficient value of 0.941 and refine the segmentation process to a higher level of precision.

Data analytics is an integral part of strategic decision making in various fields but not limited to business, education and healthcare systems. Existing research works focus on the discovery of itemsets with rare antecedents and consequent or frequent antecedents and consequent. Analysis of association among itemsets with rare antecedents and frequent consequent is equally important to gain valuable insights before making crucial decisions. Mining these itemsets from large datasets is time and resource intensive process. Expedition in the process of mining aids in quick decision making and hence, the entire dataset needs to be stored in the RAM. In this paper, a novel Weighted Binary Count Tree (WBIN-Tree) is proposed and implemented in CUDA to exploit the power of GPU and discover rules with rare antecedent and frequent consequent using parallel approach. WBIN-Tree stores the entire dataset in an abstract, complete and compact form in the RAM using single database scan. WBIN-Tree is compared with existing sequential and parallel algorithms by varying the data size and dimension. The performance evaluation of WBIN-Tree showed promising results, proving to be the most time and space efficient algorithm to store the entire large dataset in the RAM. However, based on the size of the GPU, the performance drops when executed on datasets with large dimensions which could be handled by processing the attributes in batches. Additionally, a case study is included to understand the importance of mining association rules with rare antecedent and frequent consequent by executing the algorithm on breast cancer dataset.

Fundus Fluorescein Angiography (FFA) is an important invasive diagnostic tool employed in ophthalmology for the assessment of retinal and choroidal blood flow, components situated at the rear of the eye. Identifying lesions within FFA images, indicative of anomalies in the retina and choroid visualized through the injection of a fluorescein dye, is a critical step in diagnosing and managing chronic ocular diseases. Such lesions often hint at a range of eye disorders, including but not limited to diabetic retinopathy, age-related macular degeneration, and retinal vein occlusions. In conventional practice, these images are interpreted manually by a skilled ophthalmologist. Nonetheless, due to the intricate nature of these images, the manual process is often time-consuming and subject to individual interpretation. The advent of advanced technologies has instigated the development of automated lesion detection techniques, thereby alleviating these constraints.In this study, we introduce FFA-Lens, a web-based application engineered to detect 25 prevalent lesions in FFA images, serving as a crucial resource in diagnosing and treating chronic ocular diseases. Notably, FFA-Lens has demonstrated remarkable precision and recall values exceeding 0.8 in lesion detection, emphasizing its significant potential. By enabling early detection of ocular conditions, automating lesion identification, and enhancing diagnostic efficiency, FFA-Lens can substantially improve clinical disease management strategies.

Diabetic Macular Edema (DME) represents a significant visual impairment among individuals with diabetes, leading to a dramatic reduction in visual acuity and potentially resulting in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that produces high-resolution retinal images, plays a vital role in the clinical assessment of this condition. Physicians typically rely on OCT B-Scan images to evaluate DME severity. However, manual interpretation of these images is susceptible to errors, which can lead to detrimental consequences, such as misdiagnosis and improper treatment strategies. Hence, there is a critical need for more reliable diagnostic methods. This study aims to address this gap by proposing an automated model based on Generative Adversarial Networks (GANs) to generate OCT B-Scan images of DME. The model synthesizes images from patients' baseline OCT B-Scan images, which could potentially enhance the robustness of DME detection systems. We employ five distinct GANs in this study: Deep Convolutional GAN, Conditional GAN, CycleGAN, StyleGAN2, and StyleGAN3, drawing comparisons across their performance. Subsequently, the hyperparameters of the best-performing GAN are fine-tuned using Particle Swarm Optimization (PSO) to produce more realistic OCT images. This comparative analysis not only serves to improve the detection of DME severity using OCT images but also provides insights into the appropriate choice of GANs for the effective generation of realistic OCT images from the baseline OCT datasets.

Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME.

Background-Previous studies on coronavirus disease 2019 (COVID-19) were limited to specific geographical locations and small sample sizes. Therefore, we used the National Inpatient Sample (NIS) 2020 database to determine the risk factors for severe outcomes and mortality in COVID-19. Methods-We included adult patients with COVID-19. Univariate and multivariate logistic regression was performed to determine the predictors of severe outcomes and mortality in COVID-19. Results-1,608,980 (95% CI 1,570,803–1,647,156) hospitalizations with COVID-19 were included. Severe complications occurred in 78.3% of COVID-19 acute respiratory distress syndrome (ARDS) and 25% of COVID-19 pneumonia patients. The mortality rate for COVID-19 ARDS was 54% and for COVID-19 pneumonia was 16.6%. On multivariate analysis, age > 65 years, male sex, government insurance or no insurance, residence in low-income areas, non-white races, stroke, chronic kidney disease, heart failure, malnutrition, primary immunodeficiency, long-term steroid/immunomodulatory use, complicated diabetes mellitus, and liver disease were associated with COVID-19 related complications and mortality. Cardiac arrest, septic shock, and intubation had the highest odds of mortality. Conclusions-Socioeconomic disparities and medical comorbidities were significant determinants of mortality in the US in the pre-vaccine era. Therefore, aggressive vaccination of high-risk patients and healthcare policies to address socioeconomic disparities are necessary to reduce death rates in future pandemics.

Device closure of ostium secundum atrial septal defect (ASD) has virtually replaced surgical closure except in a few circumstances. Device embolisation is a potential complication of percutaneous device closure of atrial septal defect (ASD). Every operator should be familiar of the different transcatheter retrieval techniques and hardwares. Here we report a rare occurrence of rupture of the nitinol frame (shaft) of the snare during retrieval of the embolised ASD device. Broken snare was removed, embolised device was retrieved with another snare and a new properly sized device was deployed in the same sitting.

Malignant thymoma is rarely associated with giant cell myocarditis. We present a case study that illustrates this association and cardiogenic shock with underlying tamponade. The dramatic presentation of this scenario has not been previously described.