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During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify the virus using X-ray and CT scans. This has underlined the need for advanced Deep Learning and Machine Learning approaches to effectively spot and manage the virus's spread. Indeed, researchers worldwide have dynamically participated in the field by publishing an important number of papers across various databases. In this context, we present a bibliometric analysis focused on the detection and classification of COVID-19 using Deep Learning and Machine Learning techniques, based on X-Ray and CT images. We analyzed published documents of the six prominent databases (IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect) during the period between 2019 and November 2023. Our results showed that rising forces in economy and technology, especially India, China, Turkey, and Pakistan, began to compete with the great powers in the field of scientific research, which could be seen from their number of publications. Moreover, researchers contributed to Deep Learning techniques more than the use of Machine Learning techniques or the use of both together and preferred to submit their works to Springer Database. An important result was that more than 57% documents were published as Journal Articles, which was an important portion compared to other publication types (conference papers and book chapters). Moreover, the PubMed journal Multimedia Tools and Applications' tops the list of journals with a total of 29 published articles. [ABSTRACT FROM AUTHOR]

Objective: To determine the diagnostic accuracy of a CT scan for detecting ongoing internal bleeding following torso injury, keeping operative findings as the gold standard. Study Design: Cross-sectional validation study. Place and Duration of Study: Radiology Department, Pakistan Institute of Medical Sciences, Islamabad Pakistan, from Jul 2021 to Oct 2021. Methodology: Contrast-enhanced CT was carried out for 100 patients with torso injury to detect the incidence of extravasated contrast material, an outcome that signifies any active haemorrhages present. The findings were then compared with the per - operative findings of the patient. Results: We detected active haemorrhages in 48 out of 100 patients on Computed Tomography. A total of 83 injury sites were recorded. Active haemorrhages were mostly visible through the spill of contrast agents in a jet stream in 61(73.4%) out of 83 injury sites. Immediate surgical intervention was performed on all the patients who were detected with active haemorrhages on CT, which confirmed the findings. In 3 patients out of 100, CT did not detect active haemorrhage, but surgical intervention showed active haemorrhage on intra-peritoneal sites. Computed Tomography had a high diagnostic accuracy for torso injuries (97.0%) with a sensitivity of 94. 11% and a specificity of 100%. Conclusion: Instantaneous surgical intervention is obligatory whenever contrast extravasation is identified on Computed Tomography (CT). [ABSTRACT FROM AUTHOR]