RESUMEN
BACKGROUND: Diagnosing and treating tonsillitis pose no significant challenge for otolaryngologists; however, it can increase the infection risk for healthcare professionals amidst the coronavirus pandemic. In recent years, with the advancement of artificial intelligence (AI), its application in medical imaging has also thrived. This research is to identify the optimal convolutional neural network (CNN) algorithm for accurate diagnosis of tonsillitis and early precision treatment. METHODS: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our CNN, with the algorithm including UNet, PSPNet, and FPN. A total of 485 pharyngoscopic images from 485 participants were included, comprising healthy individuals (133 cases), patients with the common cold (295 cases), and patients with tonsillitis (57 cases). Both color and texture features from 485 images are extracted for analysis. RESULTS: UNet outperformed PSPNet and FPN in accurately segmenting oropharyngeal anatomy automatically, with average Dice coefficient of 97.74% and a pixel accuracy of 98.12%, making it suitable for enhancing the diagnosis of tonsillitis. The normal tonsils generally have more uniform and smooth textures and have pinkish color, similar to the surrounding mucosal tissues, while tonsillitis, particularly the antibiotic-required type, shows white or yellowish pus-filled spots or patches, and shows more granular or lumpy texture in contrast, indicating inflammation and changes in tissue structure. After training with 485 cases, our algorithm with UNet achieved accuracy rates of 93.75%, 97.1%, and 91.67% in differentiating the three tonsil groups, demonstrating excellent results. CONCLUSION: Our research highlights the potential of using UNet for fully automated semantic segmentation of oropharyngeal structures, which aids in subsequent feature extraction, machine learning, and enables accurate AI diagnosis of tonsillitis. This innovation shows promise for enhancing both the accuracy and speed of tonsillitis assessments.
RESUMEN
BACKGROUND: Polycyclic aromatic hydrocarbon (PAH) metabolites have received increasing attention because several of these organic substances are highly carcinogenic or mutagenic. Exposure to PAHs is associated with many harmful health effects; however, we are not aware of any study that has explored the exposure to PAHs and urinary conditions in the general population. The present work aimed to investigate the correlation among PAH and urine flow rate (UFR). METHOD: Cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) 2009-2012 were used in our study. A total of 4172 participants and a total of nine PAH metabolites were examined. The UFR was measured as the amount of urine excreted in a period of time (mL/h). Several covariates were adjusted in linear regression models. RESULT: After adjusting for variables, the PAH metabolites in urine showed a significant correlation with UFR. Dose-dependent associations between PAH metabolites in the urine and UFR were also found. Higher quartiles of PAH metabolites in urine exhibited higher regression coefficients. CONCLUSION: Our study highlighted that PAH metabolites in urine had a strong association with decreased UFR in the US adult population. These findings support the possibility that PAH exposure is related to bladder dysfunction. Further prospective studies are warranted.