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SummaryBackgroundCerebral venous thrombosis is a relatively uncommon neurologic disorder that is potentially reversible with prompt diagnosis and appropriate medical care. The pathogenesis is multifactorial and the disease may occur at any age. CVT is often associated with nonspecific symptoms. Radiologists play a crucial role in patient care by providing early diagnosis through interpretation of imaging studies.Underdiagnosis or misdiagnosis can increase the risk of severe complications, including hemorrhagic stroke or death. The purpose of this study is to investigate radiological and clinical characteristics of cerebral venous thrombosis (CVT) based on material from 34 patients under care of our hospital.Material/MethodsA total of 34 patients were diagnosed with CVT from August 2009 until March 2015. A clinical and radiological database of patients with final diagnosis of CVT was analyzed.ResultsPatient group included 22 women and 12 men at a mean age of 48.7 years (ranging from 27 to 77 years). In the study group 8 patients (23.5%) suffered from hemorrhagic infarction, whereas 16 patients (47%) were diagnosed with venous infarction without hemorrhage. Thirty patients (88%) had transverse sinus thrombosis.ConclusionsAccording to our study, CVT was more prevalent in women. Transverse sinus was the most common location. Among all age groups, the highest prevalence was seen in the fifth decade (n=14). Contrast-enhanced CT and MR venography were the most sensitive imaging modalities.

The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today. In a short period of time, it has led to the development of many deep neural network models for COVID-19 detection with modules for explainability. In this work, we carry out a systematic analysis of various aspects of proposed models. Our analysis revealed numerous mistakes made at different stages of data acquisition, model development, and explanation construction. In this work, we overview the approaches proposed in the surveyed Machine Learning articles and indicate typical errors emerging from the lack of deep understanding of the radiography domain. We present the perspective of both: experts in the field - radiologists and deep learning engineers dealing with model explanations. The final result is a proposed checklist with the minimum conditions to be met by a reliable COVID-19 diagnostic model.

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