Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first-and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions.Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.

Basilar artery fenestration is the most frequent observed fenestration in CTA, followed by anterior cerebral artery and anterior communicating artery fenestrations. Coexistence of fenestration and aneurysm is uncommon in CTA examination.

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The aim of the study was to describe the value of High Resolution Computed Tomography (HRCT) with MPR and VR reconstruction of the temporal bone in patients with persistent vertigo after stapedotomy. High Resolution Computed Tomography with MPR and VR reconstruction of the temporal bone in the axial and coronal planes with 0.625 - mm slice thickness were performed in 2 patients with persistent vertigo after stapedotomy. Persistent vertigo were observed in 2 patients suffered from otosclerosis several months after stapedotomy. High Resolution Computed Tomography with MPR and VR reconstruction of the temporal bone showed in both cases too long stapes prosthesis. On the base of HRCT results restapedotomy and length reduction of stapes prosthesis were done. The vertigo was resolved in all the cases with revision surgery. HRCT with MPR and VR reconstruction can diagnosed the possible cause of persistent vertigo in patients after stapedotomy.

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