The future of CT: deep learning reconstruction.

There have been substantial advances in computed tomography (CT) technology since its introduction in the 1970s. More recently, these advances have focused on image reconstruction. Deep learning reconstruction (DLR) is the latest complex reconstruction algorithm to be introduced, which harnesses advances in artificial intelligence (AI) and affordable supercomputer technology to achieve the previously elusive triad of high image quality, low radiation dose, and fast reconstruction speeds. The dose reductions achieved with DLR are redefining ultra-low-dose into the realm of plain radiographs whilst maintaining image quality. This review aims to demonstrate the advantages of DLR over other reconstruction methods in terms of dose reduction and image quality in addition to being able to tailor protocols to specific clinical situations. DLR is the future of CT technology and should be considered when procuring new scanners.

Layered stigma among health-care and social service providers toward key affected populations in Jamaica and The Bahamas.

While considerable research has documented stigma toward key populations affected by HIV and AIDS - men who have sex with men (MSM), sex workers (SWs) - it provided limited empirical evidence on the presence of layered stigma among health-care professionals providing services for these populations. C-Change conducted a survey among 332 staff of health-care and social service agencies in Jamaica and The Bahamas to understand the levels of stigma toward people living with HIV (PLHIV), including MSM and SWs and factors associated with stigma. While most health-care professionals responding to the survey said that PLHIV, MSM, and SWs deserved quality care, they expressed high levels of blame and negative judgments, especially toward MSM and SWs. Across a stigma assessment involving eight vignette characters, the highest levels of stigma were expressed toward PLHIV who were also MSM or SWs, followed by PLHIV, MSM, and SWs. Differences were assessed by gender, country, type of staff, type of agency, and exposure to relevant training. Findings indicate higher reported stigma among nonclinical vs. clinical staff, staff who worked in general vs. MSM/SW-friendly health facilities, and among untrained vs. training staff. This implies the need for targeted staff capacity strengthening as well as improved facility environments that are MSM/SW-friendly.