A late consequence of COVID-19, organizing pneumonia is characterized by significant imaging and pathological abnormalities. The goals of this study are to better understand these abnormalities. The use of corticoid continues to be the recommended course of treatment for COVID-19. On the other hand, it is not clear whether or not corticoid has the same impact on organizing pneumonia after COVID-19. A 53-year-old male patient was identified with organized pneumonia following COVID-19 infection. He was diagnosed after experiencing severe respiratory symptoms several days with no improvement. We initiated a high dose of corticoid based on imaging and pathological findings and observed a significant response. In addition, we looked into the research that has been done concerning the diagnosis and treatment of this peculiar ailment. Patients who have been diagnosed with pneumonia after COVID 19 are required to undergo a reevaluation that includes a chest CT scan, and some of these patients may be candidates for an early lung biopsy. The most effective and convincing therapy for COVID-19-induced organizing pneumonia is corticoid treatment at a dose equivalent to 0.5 mg/kg/day of prednisone.
Type B Hepatic encephalopathy (HE) due to a congenital extra-hepatic porto-systemic shunt is an extremely rare condition. We report the case of a 57-year-old woman, with recurrent episodes of confusion and neuropsychiatric symptoms, who had an elevated serum ammonia level and a superior mesenteric-caval shunt documented on abdominal computed topography (CT) scan. There was no evidence of cirrhosis or portal hypertension. A diagnosis of non-cirrhotic, non-portal hypertension hepatic encephalopathy was made after excluding other causes of confusion and cognitive impairment. The patient was successfully treated by radiologically guided endovascular shunt closure and during 9 months follow up, her neuropsychiatric symptoms did not recur and repeated serum ammonia level results were normal.
Background: Non-contrast computed tomography of the brain (NCCTB) is commonly used in clinical practice to detect intracranial pathology but is subject to interpretation errors. Machine learning is capable of augmenting clinical decision making and there is an opportunity to apply deep learning to improve the clinical interpretation of NCCTB scans. This retrospective detection accuracy study assessed the performance changes of radiologists assisted by a deep learning model designed to identify many NCCTB clinical findings and also compared the standalone performance of the model with that of unassisted radiologists. Methods: A deep learning model was trained on 212,484 CT scan images of the brain. Thirty-two radiologists each reviewed 2,848 NCCTB cases in a test dataset with and without the assistance of the deep learning model. The consensus of three subspecialist neuroradiologists with access to reports and clinical history was used as a ground truth baseline for comparison. Performance metrics including area under the receiver operating characteristic curve (AUC) were calculated for the unassisted and assisted radiologists. Average assisted and unassisted radiologist performance was also compared to that of the model for each clinical finding. Findings: Use of the deep learning model by radiologists significantly improved interpretation performance. Assisted and unassisted radiologists demonstrated an average AUC of 0.79 and 0.73 across the 22 grouped parent findings and 0.72 and 0.68 across all 189 child findings combined, respectively. When the model was used as an assistant, change in radiologist AUC was positive and significant for 91 child findings and 158 findings were clinically non-inferior. AUC decrements were identified for 17 findings. The model alone demonstrated an average AUC of 0.93 across all 144 model findings. Interpretation: The assistance of a comprehensive NCCTB deep learning model in a non-clinical setting significantly improved radiologist detection accuracy across a wide range of clinical findings. This study demonstrated the potential of the evaluated model to improve NCCTB interpretation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.