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Objective
This study explores the potential use of drones in searching for and locating victims and of motorized transportation of search and rescue providers in a mountain environment using a simulation model.Methods
This prospective randomized simulation study was performed in order to compare two different search and rescue techniques in searching for an unconscious victim on snow-covered ground. In the control arm, the Classical Line Search Technique (CLT) was used, in which the search is performed on foot and the victim is reached on foot. In the intervention arm, the Drone-snowmobile Technique (DST) was used, the search being performed by drone and the victim reached by snowmobile. The primary outcome of the study was the comparison of the two search and rescue techniques in terms of first human contact time.Results
Twenty search and rescue operations were conducted in this study. Median time to arrival at the mannequin was 57.3 min for CLT, compared to 8.9 min for DST. The median value of the total searched area was 88,322.0 m2 for CLT and 228,613.0 m2 for DST. The median area searched per minute was 1489.6 m2 for CLT and 32,979.9 m2 for DST (p < 0.01 for all comparisons).Conclusions
In conclusion, a wider area can be searched faster by drone using DST compared to the classical technique, and the victim can be located faster and reached earlier with rescuers transported by snowmobile. 相似文献Hypophysitis is a heterogeneous condition that includes inflammation of the pituitary gland and infundibulum, and it can cause symptoms related to mass effects and hormonal deficiencies. We aimed to evaluate the potential role of machine learning methods in differentiating hypophysitis from non-functioning pituitary adenomas.
MethodsThe radiomic parameters obtained from T1A-C images were used. Among the radiomic parameters, parameters capable of distinguishing between hypophysitis and non-functioning pituitary adenomas were selected. In order to avoid the effects of confounding factors and to improve the performance of the classifiers, parameters with high correlation with each other were eliminated. Machine learning algorithms were performed with the combination of gray-level run-length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray-level co-occurrence entropy.
ResultsA total of 34 patients were included, 17 of whom had hypophysitis and 17 had non-functioning pituitary adenomas. Among the 38 radiomics parameters obtained from post-contrast T1-weighted images, 10 tissue features that could differentiate the lesions were selected. Machine learning algorithms were performed using three selected parameters; gray level run length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray level co-occurrence entropy. Error matrices were calculated by using the machine learning algorithm and it was seen that support vector machines showed the best performance in distinguishing the two lesion types.
ConclusionsOur analysis reported that support vector machines showed the best performance in distinguishing hypophysitis from non-functioning pituitary adenomas, emphasizing the importance of machine learning in differentiating the two lesions.
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