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Detecting lung pathologies is critical for precise medical diagnosis. In the realm of diagnostic methods, various approaches, including imaging tests, physical examinations, and laboratory tests, contribute to this process. Of particular note, imaging techniques like X-rays, CT scans, and MRI scans play a pivotal role in identifying lung pathologies with their non-invasive insights. Deep learning, a subset of artificial intelligence, holds significant promise in revolutionizing the detection and diagnosis of lung pathologies. By leveraging expansive datasets, deep learning algorithms autonomously discern intricate patterns and features within medical images, such as chest X-rays and CT scans. These algorithms exhibit an exceptional capacity to recognize subtle markers indicative of lung diseases. Yet, while their potential is evident, inherent limitations persist. The demand for abundant labeled data during training and the susceptibility to data biases challenge their accuracy. To address these formidable challenges, this research introduces a tailored computer-assisted system designed for the automatic retrieval of annotated medical images that share similar content. At its core lies an intelligent deep learning-based features extractor, adept at simplifying the retrieval of analogous images from an extensive chest radiograph database. The crux of our innovation rests upon the fusion of YOLOv5 and EfficientNet within the features extractor module. This strategic fusion synergizes YOLOv5's rapid and efficient object detection capabilities with EfficientNet's proficiency in combating noisy predictions. The result is a distinctive amalgamation that redefines the efficiency and accuracy of features extraction. Through rigorous experimentation conducted on an extensive and diverse dataset, our proposed solution decisively surpasses conventional methodologies. The model's achievement of a mean average precision of 0.488 with a threshold of 0.9 stands as a testament to its effectiveness, overshadowing the results of YOLOv5 + ResNet and EfficientDet, which achieved 0.234 and 0.257 respectively. Furthermore, our model demonstrates a marked precision improvement, attaining a value of 0.864 across all pathologies—a noteworthy leap of approximately 0.352 compared to YOLOv5 + ResNet and EfficientDet. This research presents a significant stride toward enhancing radiologists' workflow efficiency, offering a refined and proficient tool for retrieving analogous annotated medical images. [ABSTRACT FROM AUTHOR]

We estimate the lifetime attributable risk (LAR) of lung cancer incidence in symptomatic Coronary Artery Disease (CAD) patients receiving enhanced Coronary Computed Tomography Angiography (CCTA) and the unenhanced Computed Tomography Calcium Scoring (CTCS) examination. Retrospective analysis has been made of CCTA and CTCS data collected for 87 confirmed CAD adult patients. Patient effective dose (E) and organ doses (ODs) were calculated using CT-EXPO. Statistical correlation and the differences between E and ODs in enhanced CCTA and unenhanced CTCS were calculated using the Pearson coefficient and Wilcoxon unpaired t-test. Following BEIR VII report guidance, organ-specific LARs for the cohort were estimated using the organ-equivalent dose-to-risk conversion factor for numbers of cases per 100,000 patients exposed to low doses of 0.1 Gy. Significant statistical difference (p<0.0001) is found between E obtained for CTCS and that of CCTA. The scan length was found to be greater in CCTA (17.5 ± 2.9 cm) compared to that for CTCS (15 ± 2 cm). More elevated values of dose were noted for the esophagus (4.2 ± 2.15 mSv) and thymus (9.6 ± 2.54 mSv) for both CTCS and CCTA. CTCS organ doses were lower than that of CCTA. Per 100,000 patients, female cumulative doses are seen to give rise to greater lung cancer LARs compared to that for males, albeit with risk varying significantly, noticeably greater for females, younger patients and combined CCTA and CTCS scans. While scan parameters and tube-modulation methods clearly contribute to patient dose, mAs offers by far the greater contribution. [ABSTRACT FROM AUTHOR]

The lung is a vital organ that houses the alveoli, which is where gas exchange takes place. The COVID-19 illness attacks lung cells directly, creating significant inflammation and resulting in their inability to function. To return to the nature of their job, it may be essential to rejuvenate the afflicted lung cells. This is difficult because lung cells need a long time to rebuild and resume their function. Biopolymeric particles are the most effective means to transfer developing treatments to airway epithelial cells and then regenerate infected lung cells, which is one of the most significant symptoms connected with COVID-19. Delivering biocompatible and degradable natural biological materials, chemotherapeutic drugs, vaccines, proteins, antibodies, nucleic acids, and diagnostic agents are all examples of these molecules' usage. Furthermore, they are created by using several structural components, which allows them to effectively connect with these cells. We highlight their most recent uses in lung tissue regeneration in this review. These particles are classified into three groups: biopolymeric nanoparticles, biopolymeric stem cell materials, and biopolymeric scaffolds. The techniques and processes for regenerating lung tissue will be thoroughly explored. [ABSTRACT FROM AUTHOR]