https://www.selleckchem.com/products/alpha-conotoxin-gi.html 99, p  less then  0.01), and image noise (χ2, 21.20, p  less then  0.01) due to marker presence. In 93% of cases, the median score of observers "agree" with the statement that marker-induced noise did not influence image interpretability. Marker presence did not interfere with confidence in diagnosis (χ2, 6.00, p = 0.20). CONCLUSION Inexpensive, easy producible skin markers can be used for accurate lesion marking in automated ultrasound examinations of the breast while image interpretability is preserved. Any marker-induced noise and decreased image quality did not affect confidence in providing a diagnosis. KEY POINTS • The use of a skin marker enables the reporting radiologist to identify a location which a patient is concerned about. • The developed skin marker can be used for accurate breast lesion marking in ultrasound examinations.OBJECTIVES To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images. METHODS In this Institutional Review Board-approved retrospective study, patients who received both unenhanced CT examinations and dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae, in two institutions (1 and 2), were included. Supervised deep learning was employed to obtain a convolutional neural network (CNN) model using axial CT images, including the lumbar vertebrae as input data and BMD values obtained with DXA as reference data. For this purpose, 1665 CT images from 183 patients in institution 1, which were augmented to 99,900 (= 1665 × 60) images (noise adding, parallel shift and rotation were performed), were used. Internal (by using data of 45 other patients in institution 1) and external validations (by using data of 50 patients in institution 2) were performed to evaluate the performance of the trained CNN modeh performance.OBJECTIVES When increasing the PET acquisition time