Reproducibility and Repeatability of Intravoxel Incoherent Motion MRI Acquisition Methods in Liver.

Background: Intravoxel incoherent motion (IVIM) diffusion weighted MRI (DWI) has potential for evaluating hepatic fibrosis but image acquisition technique influence on diffusion parameter estimation bears investigation.

Purpose: To minimize variability and maximize repeatably in abdominal DWI in terms of IVIM parameter estimates.

Study Type: Prospective test-retest and image quality comparison.

Combination therapy with pioglitazone/exenatide/metformin reduces the prevalence of hepatic fibrosis and steatosis: The efficacy and durability of initial combination therapy for type 2 diabetes (EDICT).

Aim: To compare the efficacy of triple therapy (metformin/exenatide/pioglitazone) versus stepwise conventional therapy (metformin → glipizide → glargine insulin) on liver fat content and hepatic fibrosis in newly diagnosed, drug-naïve patients with type 2 diabetes.

Methods: Sixty-eight patients completed the 6-year follow-up and had an end-of-study (EOS) FibroScan to provide measures of steatosis (controlled attenuation parameter [CAP] in dB/m) and fibrosis (liver stiffness measurement [LSM] in kPa); 59 had magnetic resonance imaging-proton density fat fraction (MRI-PDFF) to measure liver fat.

Results: At EOS, HbA1c was 6.

Fears of disclosure and misconceptions regarding domestic violence reporting amongst patients in two US emergency departments.

Patients often do not disclose domestic violence (DV) to healthcare providers in emergency departments and other healthcare settings. Barriers to disclosure may include fears and misconceptions about whether, and under what circumstances, healthcare providers report DV to law enforcement and immigration authorities. We sought to assess undocumented Latino immigrants (UDLI), Latino legal residents/citizens (LLRC) and non-Latino legal residents/citizens (NLRC) beliefs about disclosure of DV victimization to healthcare providers and healthcare provider reporting of DV to law enforcement and immigration authorities.

Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models.

Artificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational "stress tests".