Titre : 2-Cyano-acrylate d'isobutyle

2-Cyano-acrylate d'isobutyle : Questions médicales fréquentes

Termes MeSH sélectionnés :

Image Processing, Computer-Assisted
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un environnement stérile." } }, { "@type": "Question", "name": "Le Bucrylate est-il utilisé pour traiter des infections ?", "position": 7, "acceptedAnswer": { "@type": "Answer", "text": "Non, il est utilisé pour la fermeture des plaies, pas pour traiter les infections." } }, { "@type": "Question", "name": "Comment le Bucrylate favorise-t-il la cicatrisation ?", "position": 8, "acceptedAnswer": { "@type": "Answer", "text": "Il forme une barrière protectrice qui aide à maintenir l'humidité et à réduire l'infection." } }, { "@type": "Question", "name": "Quelles complications peuvent survenir avec le Bucrylate ?", "position": 9, "acceptedAnswer": { "@type": "Answer", "text": "Des infections, des réactions allergiques ou des cicatrices anormales peuvent survenir." } }, { "@type": "Question", "name": "Le Bucrylate peut-il causer des réactions systémiques ?", "position": 10, "acceptedAnswer": { "@type": "Answer", "text": "Des réactions systémiques sont rares, mais possibles en cas d'allergie sévère." } }, { "@type": "Question", "name": "Qui est à risque d'allergie au Bucrylate ?", "position": 11, "acceptedAnswer": { "@type": "Answer", "text": "Les personnes ayant des antécédents d'allergies aux adhésifs ou aux cyanoacrylates." } }, { "@type": "Question", "name": "Les enfants peuvent-ils utiliser le Bucrylate ?", "position": 12, "acceptedAnswer": { "@type": "Answer", "text": "Oui, mais avec prudence et sous supervision médicale en raison de leur peau sensible." } } ] } ] }

Sources (10000 au total)

An unsupervised image registration method employing chest computed tomography images and deep neural networks.

Deformable image registration is crucial for multiple radiation therapy applications. Fast registration of computed tomography (CT) lung images is challenging because of the large and nonlinear deform... In this study, an unsupervised lung registration network (LRN) with cycle-consistent training is proposed to align two acquired CT-derived lung datasets during breath-holds at inspiratory and expirato... The results showed that the LRN with an average TRE of 1.78 ± 1.56 mm outperformed VoxelMorph with an average TRE of 2.43 ± 2.43 mm, which is comparable to that of SSTVD with an average TRE of 1.66 ± ... Therefore, this proposed method shows significant potential for use in time-sensitive pulmonary studies, such as lung motion tracking and image-guided surgery....

Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography.

To perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to asses... Fifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength l... DLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR... DLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD....

Automatic detection of A-line in lung ultrasound images using deep learning and image processing.

Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A-line is one of the most common indicators of LUS that can offer support for ... In order to solve the disadvantages of traditional A-line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A-l... First, the improved Faster R-CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for in... After 100 epochs, the mean loss value of training and validation set of improved Faster R-CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localiza... The proposed method combining image processing and deep learning can automatically and accurately detect A-line in LUS images with different probe types, which has important application value for clin...