ABSTRACT
Artificial intelligence may be used to identify COVID-19 pneumonia (also known as pneumococcal meningitis) (AI). AI algorithms are also under scrutiny for their resilience and vulnerability, as are the datasets and research methods used to get the data. AI-driven COVID-19 pneumonia detectors that use our own data from retrospective clinical studies might help overcome these difficulties. In order to assess statistically the research designs, we optimized five deep learning architectures, applied development techniques by altering data distribution and introduced several detection scenarios to test the durability and diagnostic performance of the models. To a greater extent than the present data volume, detection model performance is influenced by hyper parameter adjustment. Sn, sp, and PPV are the three most important metrics in a two-class detection situation, and a method called InceptionV3 has the best of all three. It was shown that models had improved overall performance, with 91-96 percent Sn and 94-98 percent Sp and 91-96 PPV, compared to three-class detection results. Accuracy, F1 scores and g means are all higher than 96% accurate in InceptionV3, according to InceptionV3. For the identification of COVID-19 pneumonia, InceptionV3 had the greatest results, with an AUC of 99. An AUC of 0.98 distinguishes CoVID-19 pneumonia from other kinds of pneumonia, and a micro-average of 0.99 was achieved for the remaining classes.
ABSTRACT
BACKGROUND: The SARS-CoV-2 omicron variant produces more symptoms in the upper respiratory tract than in the lower respiratory tract. This form of "common cold" can cause inflammation of the oropharynx and the Eustachian tube, leading to the multiplication of bacteria such as Streptococcus pneumoniae in the oropharynx. Eustachian tube dysfunction facilitates migration of these bacteria to the middle ear, causing inflammation and infection (otitis media), which in turn could lead to further complications such as acute mastoiditis and meningitis. CASE PRESENTATION: In January 2022, during the rapid spread of the omicron variant of the SARS-CoV-2 virus, two patients presented to the emergency room at our hospital complaining of headache and a low level of consciousness. A few days prior to admission, the patients had been diagnosed with COVID-19 based on clinical manifestations of a cold virus, without respiratory failure. Cranial computed tomography revealed signs of bilateral invasion of the middle ear in both cases. Lumbar puncture was compatible with acute bacterial meningitis, and S. pneumoniae was isolated in cerebrospinal fluid in both patients. RT-PCR tests for SARS-CoV-2 were repeated, confirming the presence of the omicron variant in one of the patients. We were unable to confirm the variant in the second patient due to the low viral load in the nasopharyngeal sample obtained at admission. However, the time of diagnosis (i.e., during the peak spread of the omicron variant), strongly suggest the presence of the omicron variant. Both patients were admitted to the intensive care unit and both showed rapid clinical improvement after initiation of antibiotic treatment. CONCLUSIONS: The omicron variant of the SARS-CoV-2 virus can promote the development of otitis media and secondary acute bacterial meningitis. S. pneumoniae is one of the main bacteria involved in this process.