BACKGROUND Contrast-enhanced ultrasound (CEUS) is considered a secondary examination compared to computed tomography (CT) and magnetic resonance imaging (MRI) in the diagnosis of hepatocellular carcinoma (HCC), due to the risk of misdiagnosing intrahepatic cholangiocarcinoma (ICC). The introduction of CEUS Liver Imaging Reporting and Data System (CEUS LI-RADS) might overcome this limitation. Even though data from the literature seems promising, its reliability in real-life context has not been well-established yet. AIM To test the accuracy of CEUS LI-RADS for correctly diagnosing HCC and ICC in cirrhosis. METHODS CEUS LI-RADS class was retrospectively assigned to 511 nodules identified in 269 patients suffering from liver cirrhosis. The diagnostic standard for all nodules was either biopsy (102 nodules) or CT/MRI (409 nodules). Common diagnostic accuracy indexes such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were assessed for the following associations: CEUS LR-5 and HCC; CEUS LR-4 and 5 merged class and HCC; CEUS LR-M and ICC; and CEUS LR-3 and malignancy. The frequency of malignant lesions in CEUS LR-3 subgroups with different CEUS patterns was also determined. Inter-rater agreement for CEUS LI-RADS class assignment and for major CEUS pattern identification was evaluated. RESULTS CEUS LR-5 predicted HCC with a 67.6% sensitivity, 97.7% specificity, and 99.3% PPV ( P < 0.001). The merging of LR-4 and 5 offered an improved 93.9% sensitivity in HCC diagnosis with a 94.3% specificity and 98.8% PPV ( P < 0.001). CEUS LR-M predicted ICC with a 91.3% sensitivity, 96.7% specificity, and 99.6% NPV ( P < 0.001). CEUS LR-3 predominantly included benign lesions (only 28.8% of malignancies). In this class, the hypo-hypo pattern showed a much higher rate of malignant lesions (73.3%) than the iso-iso pattern (2.6%). Inter-rater agreement between internal raters for CEUS-LR class assignment was almost perfect ( n = 511, k = 0.94, P < 0.001), while the agreement among raters from separate centres was substantial ( n = 50, k = 0.67, P < 0.001). Agreement was stronger for arterial phase hyperenhancement (internal k = 0.86, P < 2.7 × 10 -214 ; external k = 0.8, P < 0.001) than washout (internal k = 0.79, P < 1.6 × 10 -202 ; external k = 0.71, P < 0.001). CONCLUSION CEUS LI-RADS is effective but can be improved by merging LR-4 and 5 to diagnose...
Drone images from an experimental field cropped with sugar beet with a high diffusion of weeds taken from different flying altitudes were used to develop and test a machine learning method for vegetation patch identification. Georeferenced images were combined with a hue-based preprocessing analysis, digital transformation by an image embedder, and evaluation by supervised learning. Specifically, six of the most common machine learning algorithms were applied (i.e., logistic regression, k-nearest neighbors, decision tree, random forest, neural network, and support-vector machine). The proposed method was able to precisely recognize crops and weeds throughout a wide cultivation field, training from single partial images. The information has been designed to be easily integrated into autonomous weed management systems with the aim of reducing the use of water, nutrients, and herbicides for precision agriculture.
In a climate change scenario and under a growing interest in Precision Agriculture, it is more and more important to map and record seasonal trends of the respiration of cropland and natural surfaces. Ground-level sensors to be placed in the field or integrated into autonomous vehicles are of growing interest. In this scope, a low-power IoT-compliant device for measurement of multiple surface CO2 and WV concentrations have been designed and developed. The device is described and tested under controlled and field conditions, showing ready and easy access to collected values typical of a cloud-computing-based approach. The device proved to be usable in indoor and open-air environments for a long time, and the sensors were arranged in multiple configurations to evaluate simultaneous concentrations and flows, while the low-cost, low-power (LP IoT-compliant) design is achieved by a specific design of the printed circuit board and a firmware code fitting the characteristics of the controller.
A novel electronic system is presented for 100% in-line testing of capsules in high-speed packaging machines for the pharmaceutical industry. The system exploits transmission analysis of near-infrared radiation and works at a speed compatible with production throughput of tens of parts per second. A prototype of the system has been realized and validated with pharmaceutical products. The presented solution, however, can be used in other contexts, such as, in particular, food production and small-part manufacturing.
The atmospheric pressure air plasma technology is based on the general principle of transforming the air into an ideal conductor of plasma energy thanks to the application of an electric potential difference able to ionize the molecules. Applying the principle to the human surgery, it comes to be possible to assure an energy transfer from plasma-generator devices to the human tissue in a relatively simple way: passing through the air, with exceptionally limited effects in terms of tissue heating. Such a condition is very useful to assure effective treatments in surgery: less thermal damage, fewer side effects on the patient. This is also what emerged during the use of innovative devices embedding the Airplasma® technology (by Otech Industry S.r.l.), where temperatures on human tissues were measured stably below 50°C. However, the profiles assumed by the temperature along the different electrodes during the operating conditions are rather unclear. This knowledge is essential to improve the efficiency of the electrodes (through their redesign in shapes and materials) as well as to reduce the invasiveness of surgical interventions. The present work had the purpose of characterizing the most common electrodes thanks to temperature measurements carried out by infrared sensors respect to different operating conditions. A simplified finite element model was also developed to support the optimal redesign of electrodes.
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