Improving Access to Extracranial-Intracranial Bypass Surgery in Border Hispanic Communities Utilizing Collaborative Approach Between Plastics and Neurological Surgery

INTRODUCTION: Hispanic patients such as those with Moyamoya disease are less likely to receive surgical revascularization therapy due to inequities in access (1). Our institution is a located in the Southern Texas- Mexico border region serving a largely Hispanic population. We previously referred patients for EC-IC bypass to other quaternary-care centers in Texas. While referrals were already challenging due to distance, mixed immigration status, and poor socioeconomic background of many patients;COVID-19 further exacerbated this problem with restriction of elective surgical volume. METHOD(S): A consecutive series of EC-IC bypasses performed by authors (SKD and MDLG) were retrospectively reviewed. Baseline clinical, perioperative radiographic, and post-operative outcomes were studied. All patients were offered option of a referral to a quaternary-care centers and also given local option for performing bypass surgery. Further, patients met preoperatively with both the plastic and neurological surgeon. Ultimately, decision was made by patient. RESULT(S): A total of 6 craniotomies for EC-IC bypass were performed during the study period. The diagnoses included Moyamoya in 5 cases and symptomatic intracranial atherosclerosis in one. All patients were Hispanic, female, and nonsmokers with mean age of 35.6 years. Mean preoperative HBa1c was 7.9, preoperative LDL was 82, and mean preoperative hemoglobin was 11.3. Direct bypass was performed in 40% of cases. Mean OR time was 3 hours and 7 minutes. CONCLUSION(S): We have found collaboration between plastic and neurological surgery for surgical revascularization is feasible and improved access to care for Hispanic Moyamoya disease patients residing in a border community.

An Optimized Deep Learning Model for Automatic Diagnosis of COVID-19 Using Chest X-Ray Images

COVID-19 has caused havoc throughout the world in the last two years by infecting over 455 million people. Development of automatic diagnosis software tools for rapid screening of COVID-19 via clinical imaging such as X-ray is vital to combat this pandemic. An optimized deep learning model is designed in this paper to perform automatic diagnosis on the chest X-ray (CXR) images of patients and classify them into normal, pneumonia and COVID-19 cases. A convolutional neural network (CNN) is employed in optimized deep learning model given its excellent performances in feature extraction and classification. A particle swarm optimization with multiple chaotic initialization scheme (PSOMCIS) is also designed to fine tune the hyperparameters of CNN, ensuring the proper training of network. The proposed deep learning model, namely PSOMCIS-CNN, is evaluated using a public database consists of the CXR images with normal, pneumonia and COVID-19 cases. The proposed PSOMCIS-CNN is revealed to have promising performances for automatic diagnosis of COVID-19 cases by producing the accuracy, sensitivity, specificity, precision and F1 score values of 97.78%, 97.77%, 98.8%, 97.77% and 97.77%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Performance Comparison of Convolutional Neural Network for COVID-19 Diagnosis

COVID-19 has devastated the global healthcare system as well as the world economy with more than 600 million confirmed cases and 6 million deaths globally. A timely and accurate diagnosis of the disease plays a vital role in the treatment and preventative spread of disease. Recently, deep learning such as Convolutional Neural Networks (CNNs) have achieved extraordinary results in many applications such as medical classifications. This work focuses on investigating the performance of nine state-of-the-art architectures: Alexnet, Googlenet, Inception-v3, Mobilenet-v2, Resnet-18, Resnet-50, Shufflenet, Squeezenet and Resnet-50 RCNN for COVID-19 classification by comparing with performance metrics such as accuracy, precision, sensitivity, specificity and F-score. The datasets considered in current study are divided into three different classes namely Normal Chest X-Rays (CXRs), Pneumonia patient CXR and COVID-19 patient CXR. The results achieved shows that Resnet-50 RCNN achieved an accuracy, precision, sensitivity, specificity and F-score of 95.67%, 95.71%, 95.67%, 97.84% and 95.67% respectively. © The 2023 International Conference on Artificial Life and Robotics (ICAROB2023).