Automated Track Change Detection Technology for Enhanced Railroad Safety Assessment

693JJ619C000004The Rail Transportation and Engineering Center at the University of Illinois at Urbana-Champaign and Railmetrics, Inc. evaluated the use of 3D laser scanning, Deep Convolutional Neural Networks (DCNNs), and change detection technology for railway track safety inspections. Researchers evaluated the potential use of these combined technologies to provide value-added inspection data to traditional track inspection methods. The project was conducted between April 2019 and October 2020. Field testing was completed on the High Tonnage Loop at the Transportation Technology Center in Pueblo, Colorado

Appendicitis risk prediction models in children presenting with right iliac fossa pain (RIFT study): a prospective, multicentre validation study.

Background Acute appendicitis is the most common surgical emergency in children. Differentiation of acute appendicitis from conditions that do not require operative management can be challenging in children. This study aimed to identify the optimum risk prediction model to stratify acute appendicitis risk in children. Methods We did a rapid review to identify acute appendicitis risk prediction models. A prospective, multicentre cohort study was then done to evaluate performance of these models. Children (aged 5\u201315 years) presenting with acute right iliac fossa pain in the UK and Ireland were included. For each model, score cutoff thresholds were systematically varied to identify the best achievable specificity while maintaining a failure rate (ie, proportion of patients identified as low risk who had acute appendicitis) less than 5%. The normal appendicectomy rate was the proportion of resected appendixes found to be normal on histopathological examination. Findings 15 risk prediction models were identified that could be assessed. The cohort study enrolled 1827 children from 139 centres, of whom 630 (34\ub75%) underwent appendicectomy. The normal appendicectomy rate was 15\ub79% (100 of 630 patients). The Shera score was the best performing model, with an area under the curve of 0\ub784 (95% CI 0\ub782\u20130\ub786). Applying score cutoffs of 3 points or lower for children aged 5\u201310 years and girls aged 11\u201315 years, and 2 points or lower for boys aged 11\u201315 years, the failure rate was 3\ub73% (95% CI 2\ub70\u20135\ub72; 18 of 539 patients), specificity was 44\ub73% (95% CI 41\ub74\u201347\ub72; 521 of 1176), and positive predictive value was 41\ub74% (38\ub75\u201344\ub74; 463 of 1118). Positive predictive value for the Shera score with a cutoff of 6 points or lower (72\ub76%, 67\ub74\u201377\ub74) was similar to that of ultrasound scan (75\ub70%, 65\ub73\u201383\ub71). Interpretation The Shera score has the potential to identify a large group of children at low risk of acute appendicitis who could be considered for early discharge. Risk scoring does not identify children who should proceed directly to surgery. Medium-risk and high-risk children should undergo routine preoperative ultrasound imaging by operators trained to assess for acute appendicitis, and MRI or low-dose CT if uncertainty remains. Funding None