Endoscopic dilation of benign post-esophagectomy anastomotic strictures: long-term outcomes and risk of recurrence.

Background: Benign esophageal anastomotic strictures often require repeat dilation to relieve dysphagia. Little is known about the factors that affect their remediation. The purpose of this article was to retrospectively evaluate the long-term clinical results of endoscopic dilation in the treatment of benign anastomotic strictures after esophagectomy, and to identify factors associated with stricture recurrence.

Patient monitoring as a predictor of blood culture results in a tertiary neonatal intensive care unit.

We present a mesoergonomic approach to the early detection of neonatal sepsis, analyzing clinical data for 4999 patients from a neo-natal intensive care unit to predict positive culture results. The Apgar score at birth predicted positive results. For neonates with poor and intermediate Apgar scores, culture results for monitored infants were more likely to be positive than those for unmonitored infants.

Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

Background: The National Lung Screening Trial (NLST) demonstrated that annual screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. Nonetheless, the leading cause of mortality in the study was from cardiovascular diseases.

Purpose: To determine whether the used machine learning automatic algorithms assessing coronary calcium score (CCS), level of liver steatosis and emphysema percentage in the lungs are good predictors of cardiovascular disease (CVD) mortality and incidence when applied on low dose CT scans.

Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization.

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA) and risk predictors like the Fracture Risk Assessment Tool (FRAX), are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex.

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade.

Purpose: Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized. In contrast to DEXA, CT utilization is high and presents a valuable data source for opportunistic osteoporosis screening.