Covid 19 Treatment through Advanced Artificial Neural Network Algorithm

The development of computer science has been phenomenal. This computer development is exerting its dominance in all fields. In today's world, the need for computer usage is increasing. All departments are computerized in some way for their use. Every day in human life new diseases is attacking man. Covid19 disease confined all the people inside the house. This impacted the economy of all the people and affected the development of all sectors. Due to this, with the development of the computer industry, this research paper aims to cure the effect of this disease and detect its functions easily. In this we find solutions using Advanced Artificial neural network Algorithm applications

Role of Chest X-ray abnormalities in predicting outcome of COVID-19 in Young Adult Patient

COVID-19 is a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Recently COVID -19 radiological literature focuses primarily on CT scan findings which are more sensitive (about 97%) and specific than chest x-ray. But it has to be remembered that performing CT scan is not easy during this pandemic situation. So, the aim of the study was to analyze the chest x-ray severity scoring system and its association with outcome in a young adult patient with COVID-19. This cross-sectional study was carried out from September 15 to December 31 2020 in the COVID unit of BSMMU and it included 100 RT-PCR positive COVID-19 patients according to selection criteria. Chest x-ray postero-anterior view was done in the radiology department of BSMMU. Each patient’s chest x-ray was examined by a radiologist and a pulmonologist with experience of 10 years. Radiological scoring was done by using a scoring system. All patients were followed after 20 days from the first presentation to see the outcome. Out of 100 patients, 73 patients (73%) needed hospital admission, 33(33%) patients were hospitalized but did not developed sepsis, 29 (29%) patient developed sepsis, 10(10%) patient needed ICU support among them 2 patients got intubation. 1(1%) patient was dead. Radiological score ≥ 4 was associated with increased risk of hospitalization. (Area under curve = 0.956). Score ≥ 5 was associated with increased risk of sepsis; score ≥7 was associated with increased risk of ICU admission. (p-value<0.001). BSMMU J 2021; 14 (COVID -19 Supplement): 30-3

Deep learning of chest X‑rays can predict mechanical ventilation outcome in ICU‑admitted COVID‑19 patients

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model