Is artificial rupture of membranes considered augmentation?
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| Papers (10) | Insight |
|---|---|
18 Citations | It is concluded that rupture of membranes does not significantly increase the incidence of early deceleration patterns. |
Open access•Journal Article | Titanium-reinforced e-PTFE membranes can be satisfactorily used for vertical augmentation of atrophic ridges. |
| The reduced ultimate strength and Young's modulus of elasticity at rupture in regenerated membrane as compared with normal membrane is considered to be due to the preponderance of thinner filaments in the former. | |
279 Citations | Recently, it has become clear that rupture of the fetal membranes, term or preterm, is not merely the result of the stretch and shear forces of uterine contractions, but is, in significant part, the consequence of a programmed weakening process. |
12 Citations | Non-degradable membranes have been used for ridge augmentation with encouraging results however; requirement of second surgery for its removal and associated infection on exposure may compromise the desired results. |
11 Citations | Artificial rupture of the membranes has proved satisfactory in most cases, but a definite group remains in which our methods leave much to be desired. |
| Premature rupture of membranes significantly affects various outcome measures when delivery is induced, particularly the induction-to-delivery interval. | |
119 Citations | This study suggests that there may be inherent differences between membranes which rupture prematurely and those which do not. |
13 Citations | This technique for sealing ruptured membranes is effective after amniocentesis, but may not be of benefit with spontaneous rupture. |
11 Citations | The process leading to weakening of the membranes at the site of rupture, would probably cause similar changes in other areas of the membranes as well. |
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What is data augmentation?4 answersData augmentation is a technique used to generate new data sets from existing ones and artificially increase the size of a dataset. It involves various methods such as image rotating, cropping, flipping, and adding noise to audio signals to enhance the data. The main goal of data augmentation is to provide the model with more training data, which can enhance the performance of the proposed model and improve generalization for unknown data. It also helps reduce overfitting by increasing the number of training data included in the machine learning model. Data augmentation is particularly useful in dealing with data scarcity and lack of diversity, and it is commonly employed in computer vision and natural language processing (NLP) models. By employing data augmentation techniques, the robustness and accuracy of machine learning models can be improved, leading to better performance and generalization.
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