Virtual/Mobile Overdose Response in Canada: A Social Return on Investment Analysis.

Objectives: The overdose epidemic continues to be one of the leading causes of death in North America and continues to contribute to high healthcare costs. Although harm reduction initiatives have significantly reduced the aforementioned costs, there is a dearth of evidence regarding overdose response hotlines and applications. We aim to evaluate the social return on investment from a payer perspective of one such overdose response hotline, Canada's National Overdose Response Service, and its implications for service users, service operators, the Canadian healthcare system, and program funders.

A wealth of structures for the GeH radical cation: comparison of theory and experiment.

Five structures of GeH and GeH are investigated in this study. Optimized geometries at the CCSD(T)/cc-pwCVQZ-PP level of theory were obtained. Focal point analyses were performed on these optimized geometries to determine relative energies using the CCSD(T) method with polarized basis sets up to quintuple-zeta.

Three-Dimensional Measurement of the Uterus on Magnetic Resonance Images: Development and Performance Analysis of an Automated Deep-Learning Tool.

Uterus measurements are useful for assessing both the treatment and follow-ups of gynaecological patients. The aim of our study was to develop a deep learning (DL) tool for fully automated measurement of the three-dimensional size of the uterus on magnetic resonance imaging (MRI). In this single-centre retrospective study, 900 cases were included to train, validate, and test a VGG-16/VGG-11 convolutional neural network (CNN).

Pregnancy in a rudimentary horn: multicenter's MRI features of a rare condition.

Purpose: To describe the MRI features of rudimentary horn pregnancy (RHP) with surgical correlations.

Methods: Nine women with a RHP underwent preoperative pelvic MRI. MRI protocol included T2- (n = 9), T1- (n = 7), and fat-suppressed contrast-enhanced T1-weighted sequences (n = 4).

Impact of artificial intelligence in breast cancer screening with mammography.

Objectives: To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time.

Methods: A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support.