Measurement of Endotracheal Tube Positioning on Chest X-Ray Using Object Detection.

Patients who are intubated with endotracheal tubes often receive chest x-ray (CXR) imaging to determine whether the tube is correctly positioned. When these CXRs are interpreted by a radiologist, they evaluate whether the tube needs to be repositioned and typically provide a measurement in centimeters between the endotracheal tube tip and carina. In this project, a large dataset of endotracheal tube and carina bounding boxes was annotated on CXRs, and a machine-learning model was trained to generate these boxes on new CXRs and to calculate a distance measurement between the tube and carina. This model was applied to a gold standard annotated dataset, as well as to all prospective data passing through our radiology system for two weeks. Inter-radiologist variability was also measured on a test dataset. The distance measurements for both the gold standard dataset (mean error = 0.70 cm) and prospective dataset (mean error = 0.68 cm) were noninferior to inter-radiologist variability (mean error = 0.70 cm) within an equivalence bound of 0.1 cm. This suggests that this model performs at an accuracy similar to human measurements, and these distance calculations can be used for clinical report auto-population and/or worklist prioritization of severely malpositioned tubes

Detection of Critical Spinal Epidural Lesions on CT Using Machine Learning.

BACKGROUND: Critical spinal epidural pathologies can cause paralysis or death if untreated. Although magnetic resonance imaging is the preferred modality for visualizing these pathologies, computed tomography (CT) occurs far more commonly than magnetic resonance imaging in the clinical setting. OBJECTIVE: A machine learning model was developed to screen for critical epidural lesions on CT images at a large-scale teleradiology practice. This model has utility for both worklist prioritization of emergent studies and identifying missed findings. MATERIALS AND METHODS: There were 153 studies with epidural lesions available for training. These lesions were segmented and used to train a machine learning model. A test data set was also created using previously missed epidural lesions. The trained model was then integrated into a teleradiology workflow for 90 days. Studies were sent to secondary manual review if the model detected an epidural lesion but none was mentioned in the clinical report. RESULTS: The model correctly identified 50.0% of epidural lesions in the test data set with 99.0% specificity. For prospective data, the model correctly prioritized 66.7% of the 18 epidural lesions diagnosed on the initial read with 98.9% specificity. There were 2.0 studies flagged for potential missed findings per day, and 17 missed epidural lesions were found during a 90-day time period. These results suggest almost half of critical spinal epidural lesions visible on CT imaging are being missed on initial diagnosis. CONCLUSION: A machine learning model for identifying spinal epidural hematomas and abscesses on CT can be implemented in a clinical workflow

Fe-Beta@CeO2 core-shell catalyst with tunable shell thickness for selective catalytic reduction of NOx with NH3

A series of core-shell structural deNO x catalysts using small-grain Beta supporting FeO x nanoparticles as the core and tunable CeO 2 thin film thickness as sheaths were designed and controllably synthesized. Their catalytic performances were tested for selective catalytic reduction of NO x with NH 3 (NH 3 -SCR). It was found that CeO 2 shell thickness plays an important role in influencing the acidity and redox properties of the catalysts. Fe-Beta at CeO 2 core-shell catalysts exhibit excellent resistance to H 2 O and SO 2 and high NO x conversion (above 90%) in the wide temperature range (225-565°C). The kinetics result indicates that the coating of CeO 2 shell significantly increases the pore diffusion resistance of Fe-Beta at CeO 2 catalysts. Furthermore, in situ DRIFT results reveal that CeO 2 shell can promote the formation of NO 2 and cis- N2O2- species. But too thick CeO 2 shell (~20 nm) would result in the formation of inactive nitrate species, and thereby lead to a decrease of high-temperature activity of the catalysts