Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks.

BACKGROUND: Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks. In this study, we aimed to develop a novel framework for locating cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNN). METHODS: We have trained our model with the dataset from the ISBI 2015 grand challenge in dental X-ray image analysis. The overall algorithm consisted of a region of interest (ROI) extraction of landmarks and landmarks estimation considering uncertainty. Prediction data produced from the Bayesian model has been dealt with post-processing methods with respect to pixel probabilities and uncertainties. RESULTS: Our framework showed a mean landmark error (LE) of 1.53 ± 1.74 mm and achieved a successful detection rate (SDR) of 82.11, 92.28 and 95.95%, respectively, in the 2, 3, and 4 mm range. Especially, the most erroneous point in preceding studies, Gonion, reduced nearly halves of its error compared to the others. Additionally, our results demonstrated significantly higher performance in identifying anatomical abnormalities. By providing confidence regions (95%) that consider uncertainty, our framework can provide clinical convenience and contribute to making better decisions. CONCLUSION: Our framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education.

Coronary artery decision algorithm trained by two-step machine learning algorithm.

A two-step machine learning (ML) algorithm for estimating both fractional flow reserve (FFR) and decision (DEC) for the coronary artery is introduced in this study. The primary purpose of this model is to suggest the possibility of ML-based FFR to be more accurate than the FFR calculation technique based on a computational fluid dynamics (CFD) method. For this purpose, a two-step ML algorithm that considers the flow characteristics and biometric features as input features of the ML model is designed. The first step of the algorithm is based on the Gaussian progress regression model and is trained by a synthetic model using CFD analysis. The second step of the algorithm is based on a support vector machine with patient data, including flow characteristics and biometric features. Consequently, the accuracy of the FFR estimated from the first step of the algorithm was similar to that of the CFD-based method, while the accuracy of DEC in the second step was improved. This improvement in accuracy was analyzed using flow characteristics and biometric features.

Safety assessment of freeze-dried powdered Tenebrio molitor larvae (yellow mealworm) as novel food source: Evaluation of 90-day toxicity in Sprague-Dawley rats.

Worldwide demand for novel food source has grown and edible insects are a promising food sources for humans. Tenebrio molitor, as known as yellow mealworm, has advantages of being rich in protein, and easy to raise as a novel food source. The objective of this study was to evaluate subchronic toxicity, including potential hypersensitivity, of freeze-dried powdered T. molitor larvae (fdTML) in male and female Sprague-Dawley rats. The fdTML was administered orally once daily at dose levels of 0, 300, 1000 and 3000 mg/kg/day for 90 days. A toxicological assessment was performed, which included mortality, clinical signs, body and organ weights, food consumption, ophthalmology, urinalysis, hematology, serum chemistry, gross findings, histopathologic examination and allergic reaction. There were no fdTML- related findings in clinical signs, urinalysis, hematology and serum chemistry, gross examination, histopathologic examination or allergic reaction. In conclusion, the No Observed Adverse Effect Level (NOAEL) for fdTML was determined to be in excess of 3000 mg/kg/day in both sexes of rats under the experimental conditions of this study.

LC50 Determination of tert-Butyl Acetate using a Nose Only Inhalation Exposure in Rats.

tert-Butyl acetate (TBAc) is an organic solvent, which is commonly used in architectural coatings and industrial solvents. It has recently been exempted from the definition of a volatile organic compound (VOC) by the Air Resources Board (ARB) . Since the use of TBAc as a substitute for other VOCs has increased, thus its potential risk in humans has also increased. However, its inhalation toxicity data in the literature are very limited. Hence, inhalation exposure to TBAc was carried out to investigate its toxic effects in this study. Adult male rats were exposed to TBAc for 4 h for 1 day by using a nose-only inhalation exposure chamber (low dose, 2370 mg/m(3) (500 ppm) ; high dose, 9482 mg/m(3) (2000 ppm) ) . Shamtreated control rats were exposed to clean air in the inhalation chamber for the same period. The animals were killed at 2, 7, and 15 days after exposure. At each time point, body weight measurement, bronchoalveolar lavage fluid (BALF) analysis, histopathological examination, and biochemical assay were performed. No treatment-related abnormal effects were observed in any group according to time course. Based on those findings, the median lethal concentration (LC50) of TBAc was over 9482 mg/m(3) in this study. According to the MSDS, the 4 h LC50 for TBAc for rats is over 2230 mg/m(3). We suggested that this value is changed and these findings may be applied in the risk assessment of TBAc which could be beneficial in a sub-acute study.