Pulmonary arteriovenous malformation in two adult alpacas (Vicugna pacos)

Two cases of pulmonary vascular anomaly in unrelated adult alpacas (Vicugna pacos) are described. In the first case, a 9-year-old intact male alpaca presented at Oregon State University Veterinary Teaching Hospital with bilateral epistaxis and died the subsequent day following severe hemorrhage from the mouth and nostrils. At necropsy, a tortuous vascular lesion was identified in the right cranial lung lobe, associated with hemorrhage into airways. In the second case, a 2-year-old female alpaca presented with postpartum anorexia, opisthotonus, and recumbency. In this second case, a similar vascular lesion was identified in the right cranial lung lobe but without associated hemorrhage. Histopathological examination of the lesion in both cases revealed numerous dilated, irregular blood vessels with marked variation in wall thickness within vessels, surrounded by foci of extramedullary hematopoiesis. Diagnoses of locally extensive pulmonary vascular anomalies (arteriovenous malformations) were made.Keywords: Arteriovenous fistula, Lung, Histopathology, Vicugna pacos, Alpacas, Epistaxi

Screening for Depression in Adults: A Summary of the Evidence for the U.S. Preventive Services Task Force

See related article on pp 760-764

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