Prevalence of Diabetic Retinopathy and Correlation with HbA1c in Patients Admitted in Khyber Teaching Hospital Peshawar

Objective: To determine the prevalence of diabetic retinopathy in patients admitted in Khyber Teaching Hospital Peshawar and to correlate different stages of diabetic retinopathy with HbA1C levels. Methodology: This cross sectional study was conducted at Department of Ophthalmology, Khyber Teaching Hospital, MTI, Peshawar from December 2019 to May 2020. All patients over the age of 15 years who were diagnosed with diabetes mellitus were included in the study while patients with cataract or retinopathy due to other pathologies were excluded. All diabetic patients were admitted through outpatient department. In the ward their blood pressures were recorded and HbA1c levels were also measured. Visual acuity (VA) was checked. Screening for diabetic retinopathy was done by a consultant ophthalmologist by Optos Ultrawide Field Imaging of retina and Optical Coherence Tomography (OCT) of macula to establish stages of diabetic retinopathy and presence of diabetic macular edema respectively. Results: A total of 103 diabetic patients were included. Their retina was photographed, viewed and analyzed. Diabetic retinopathy, irrespective of the type, was found in 69 patients with a prevalence of 66.9%. Patients with lower ranges of HbA1c (below 6%) showed no evidence of DR. The clustering of majority of patients with diabetic retinopathy with HbA1c levels of 8 to 12 %, showed a significant relationship between high blood sugar levels and severity. Conclusion: In our study the higher frequency of retinopathy is alarming by considering it one of the leading causes of blindness in working class. It is highly recommended that routine ophthalmologic examination may be carried out along with optimal diabetic control

An Attention-Based ResNet Architecture for Acute Hemorrhage Detection and Classification: Toward a Health 4.0 Digital Twin Study

Due to the advancement of digital twin (DT) technology, Health 4.0 applications have become reality and starting to take roots. In this article, we focus on intracranial hemorrhage (ICH) which is a life-threatening emergency that needs immediate diagnosis and treatment. ICH is caused by bleeding inside the skull or brain. Radiologists typically examine computed tomography (CT) scans of the patients to determine the ICH and its subtype. But the manual assessment of the CT scan is a complex and time-consuming task. The existing pre-trained convolutional neural network (CNN) models are state-of-the-art for ICH classification. However, they employ poor feature extraction techniques which hinder overall model performance. Furthermore, they suffer from the curse of dimensionality and use redundant and noisy features. The problem of imbalanced data is also crucial for achieving model generalization. This paper proposes a hybrid attention-based ResNet architecture for ICH detection and classification. An attention mechanism allows the model to focus on a specific region and extract relevant features. Principal component analysis (PCA) is used for dimensionality reduction and redundant feature removal whereas deep convolutional generative adversarial network (DCGAN) is used for resolving the class imbalance problem. The proposed model is evaluated using the dataset assembled during the Radiologist Society of North America (RSNA) ICH detection challenge 2019. The results show that our proposed model outperforms existing state-of-the-art models in terms of accuracy and F1-score. ICH classification achieved accuracies of 99.2%, 97.1%, 96.7%, 96.7% and 96.1%, for detecting epidural hemorrhage (EH), intraparenchymal hemorrhage (IH), intraventricular hemorrhage (IVH), subdural hemorrhage (SH), and subarachnoid hemorrhage (SAH) subtypes respectively. The F1-score of 96.1% for EH subtype is also best when compared with the benchmark models