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Backgrounds and objective: Evaluating the tympanic membrane (TM) using an otoendoscope is the first and most important step in various clinical fields. Unfortunately, most lesions of TM have more than one diagnostic name. Therefore, we built a database of otoendoscopic images with multiple diseases and investigated the impact of concurrent diseases on the classification performance of deep learning networks. Study design: This retrospective study investigated the impact of concurrent diseases in the tympanic membrane on diagnostic performance using multi-class classification. A customized architecture of EfficientNet-B4 was introduced to predict the primary class (otitis media with effusion (OME), chronic otitis media (COM), and 'None' without OME and COM) and secondary classes (attic cholesteatoma, myringitis, otomycosis, and ventilating tube). Results: Deep-learning classifications accurately predicted the primary class with dice similarity coefficient (DSC) of 95.19%, while misidentification between COM and OME rarely occurred. Among the secondary classes, the diagnosis of attic cholesteatoma and myringitis achieved a DSC of 88.37% and 88.28%, respectively. Although concurrent diseases hampered the prediction performance, there was only a 0.44% probability of inaccurately predicting two or more secondary classes (29/6,630). The inference time per image was 2.594 ms on average. Conclusion: Deep-learning classification can be used to support clinical decision-making by accurately and reproducibly predicting tympanic membrane changes in real time, even in the presence of multiple concurrent diseases. [ABSTRACT FROM AUTHOR]

Objectives: Although several self-reported questionnaire-based studies have found an association between smoking and moderately increased albuminuria, this result remains controversial. We investigated whether moderately increased albuminuria was associated with smoking status, verified by urinary cotinine (an objective biomarker of tobacco exposure), using population-based, nationally representative data. Methods: This study included 2059 participants aged ≥ 50 years from the 2014 Korean National Health and Nutrition Examination Survey. Individuals with a urinary cotinine level ≥ 50 ng/mL were identified as cotinine-verified smokers. Moderately increased albuminuria was defined as a urine albumin-to-creatinine ratio ranging between ≥ 30 mg/g and < 300 mg/g. Multivariable logistic regression was used to evaluate the association between cotinine-verified smoking status and moderately increased albuminuria. Results: Among the study participants, 16.9% were cotinine-verified smokers, 84.8% of whom were men. After adjustment for multiple covariates, cotinine-verified smokers showed a significant positive association with moderately increased albuminuria (adjusted odds ratio: 4.37, 95% confidence interval: 1.63–11.71) compared with cotinine-verified non-smokers. The association between urinary cotinine and moderately increased albuminuria did not differ with age, sex, obesity, or comorbidities (P-value for interaction > 0.05 in all cases). Conclusion: This large-scale observational study showed that cotinine-verified smoking is associated with moderately increased albuminuria in the Korean middle-aged and older general population, suggesting that smoking must be strictly controlled to reduce the risk of moderately increased albuminuria. [ABSTRACT FROM AUTHOR]