Imaging strategies for detection of urgent conditions in patients with acute abdominal pain: diagnostic accuracy study

Objective To identify an optimal imaging strategy for the accurate detection of urgent conditions in patients with acute abdominal pain

A comparison of the Accuracy of Ultrasound and Computed Tomography in common diagnoses causing acute abdominal pain

Head-to-head comparison of ultrasound and CT accuracy in common diagnoses causing acute abdominal pain. Consecutive patients with abdominal pain for > 2 h and <5 days referred for imaging underwent both US and CT by different radiologists/radiological residents. An expert panel assigned a final diagnosis. Ultrasound and CT sensitivity and predictive values were calculated for frequent final diagnoses. Effect of patient characteristics and observer experience on ultrasound sensitivity was studied. Frequent final diagnoses in the 1,021 patients (mean age 47; 55% female) were appendicitis (284; 28%), diverticulitis (118; 12%) and cholecystitis (52; 5%). The sensitivity of CT in detecting appendicitis and diverticulitis was significantly higher than that of ultrasound: 94% versus 76% (p <0.01) and 81% versus 61% (p = 0.048), respectively. For cholecystitis, the sensitivity of both was 73% (p = 1.00). Positive predictive values did not differ significantly between ultrasound and CT for these conditions. Ultrasound sensitivity in detecting appendicitis and diverticulitis was not significantly negatively affected by patient characteristics or reader experience. CT misses fewer cases than ultrasound, but both ultrasound and CT can reliably detect common diagnoses causing acute abdominal pain. Ultrasound sensitivity was largely not influenced by patient characteristics and reader experience

Robust real-world emissions by integrated ADF and powertrain control development

\u3cp\u3eThis work gives an outlook on the potential of automated driving functions (ADFs) to reduce real-world CO \u3csub\u3e2\u3c/sub\u3e and pollutant emissions for heavy-duty powertrains. Up to now, ADF research mainly focuses on increased traffic safety, driver comfort, and road capacity. Studies on emissions are lacking. By taking the driver out-of-the-loop, cycle-to-cycle variability is removed and energy losses and large accelerations can be significantly reduced. This enhances emission performance robustness, which will allow for more fuel-efficient engine settings. A general, optimal control framework is introduced, which integrates ADF with energy and emission management. Based on predictions of the vehicle power demand and emissions, a desired vehicle velocity profile, which minimizes the overall vehicle energy consumption, is determined. In this approach, real-world tailpipe emissions are explicitly taken into account. This opens the route to emission trading on vehicle or even, on platoon, fleet, and traffic level. For the combined ADF and powertrain development, testing, and certification, various opportunities are presented to fully exploit the synergy between these systems and to reduce development time and costs. By equipping vehicles with an emission monitoring system, real-world data of the ADF emission reduction potential becomes available. As validated traffic and component aging models are lacking, this data is also valuable for realistic scenario development and uncertainty modeling in virtual or mixed testing. This will lead to improved robustness evaluation and performance. \u3c/p\u3