Background: Pneumatization of various bones around the nasal cavity results in the formation of paranasal sinuses. Varying degrees of pneumatization result in multiple variations of paranasal sinuses some of which are important from clinical, pathological and surgical perspective. The aim of the present study was to investigate the role of MDCT in detection of paranasal variants and their surgical and clinical impact. Methods: 100 patients with MSCT of the paranasal sinuses were included in this study. Patients having indications of incessant rhinosinusitis hard-headed to restorative treatment and would be candidates for endoscopic sinus surgery were included in this study. Results: The mean age of the studied patients was 30 years. There were no significant differences between males and females regarding agger nasi cell, Bulla ethmoidalis (P-value = 1.0), Concha bullosa (P-value = 0.75), crista galli pneumatization, Deviated nasal septum (P-value = 0.208), Frontal sinus agenesis (Pvalue = 0.62), Frontal sinus hypoplasia (P-value = 0.719), Haller cell (P-value = 1.0), Inferior turbinate pneumatization, Maxillary sinus hypoplasia, Maxillary sinus septation, Onodi cells (P-value = 1.0), Paradoxical middle turbinate (P-value = 0.470), Sphenoid sinus hypoplasia (P-value = 0.497), Sphenoid sinus septation, Superior turbinate pneumatization, Supraorbital cells, and presence of at least one abnormality (P-value = 0.203). Conclusion: Inspection of MSCT scans must be performed to identify anatomical variations involving the key area of the osteomeatal complex and frontal recess should be considered before surgical interventions.

Background: Over the last few years, there has been increasing interest in the use of deep learning algorithms to assist with abnormality detection on medical images. Aim of this study was to investigate the performance of Artificial Intelligence on the detection of pathologies in chest radiographs compared with high resolution multi slice Computed Tomography. Methods: this prospective study was done on 200 cases, who underwent automatic detection of chest disease based on chest radiography in a comprehensive survey on computer-aided detection systems, focuses on the artificial intelligence technology applied in chest radiography to detect the presence of different pathologies, including pleural effusion, pneumothorax, pneumonia, pulmonary masses, and nodules in AP and PA -view chest radiographs using modern digital radiography. Using high resolution multi slice Computed Tomography (16/64/128 detector) for chest examination for abnormality detected by artificial intelligence technology. Axial scanning extending from base of the neck down below the diaphragm with coronal & sagittal reformate images. Results: The mean age of patients was 46.3 years. 123 patients (61.5 %) were males and 77 patients (38.5 %) were female. There was a statistically significant difference between CAD and MDCT diagnosed by radiologist according to sensitivity, p<0.001. Conclusion: In spite CAD system has established fair accuracy, the need of more accurate algorithm is necessary to determine if it can replicate MDCT and radiologist observation of abnormality on chest X-rays

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