Objectives: To assess root canal localization accuracy using a dynamic approach, surgical guides and freehand technique in vitro.
Materials And Methods: Access cavities were prepared for 4 different 3D printed tooth types by 4 operators (n = 144). Deviations from the planning in angle and bur positioning were compared and operating time as well as tooth substance loss were evaluated (Kruskal-Wallis Test, ANOVA). Operating method, tooth type, and operator effects were analyzed (partial eta-squared statistic).
Results: Angle deviation varied significantly between the operating methods (p < .0001): freehand (9.53 ± 6.36°), dynamic (2.82 ± 1.8°) and static navigation (1.12 ± 0.85°). The highest effect size was calculated for operating method (ηP²=0.524), followed by tooth type (0.364), and operator (0.08). Regarding deviation of bur base and tip localization no significant difference was found between the methods. Operating method mainly influenced both parameters (ηP²=0.471, 0.379) with minor effects of tooth type (0.157) and operator. Freehand technique caused most substance loss (p < .001), dynamic navigation least (p < .0001). Operating time was the shortest for freehand followed by static and dynamic navigation.
Conclusions: Guided endodontic access may aid in precise root canal localization and save tooth structure.
Clinical Relevance: Although guided endodontic access preparation may require more time compared to the freehand technique, the guided navigation is more accurate and saves tooth structure.
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http://dx.doi.org/10.1007/s00784-024-05603-8 | DOI Listing |
Adv Neonatal Care
January 2025
Author Affiliations: Neonatal Intensive Care Unit, Seattle Children's Hospital, Seattle, WA (Mrs LaBella, Ms Kelly, Mrs Carlin, and Dr Walsh); and Seattle Children's Research Institute, Seattle, WA (Mrs Carlin and Dr Walsh).
Background: Finding an accurate and simple method of thermometry in the neonatal intensive care unit is important. The temporal artery thermometer (TAT) has been recommended for all ages by the manufacturer; however, there is insufficient evidence for the use of TAT in infants, especially to detect hypothermia.
Purpose: To assess the accuracy of the TAT in hypothermic neonates in comparison to a rectal thermometer.
Invest Radiol
January 2025
From the Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany (A. Schwarz, A. Simon, A.M.); Siemens Healthineers AG, Forchheim, Germany (A. Schwarz, C.H., J.D., A. Simon); Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany (F.K.W., S.G., M.S.); and Institut for Radiology, Pediatric and Neuroradiology, Helios Hospital, Schwerin, Germany (H.-J.R.).
Objective: Respiratory motion can affect image quality and thus affect the diagnostic accuracy of CT images by masking or mimicking relevant lung pathologies. CT examinations are often performed during deep inspiration and breath-hold to achieve optimal image quality. However, this can be challenging for certain patient groups, such as children, the elderly, or sedated patients.
View Article and Find Full Text PDFPLOS Digit Health
January 2025
FIND, Geneva, Switzerland.
AI based software, including computer aided detection software for chest radiographs (CXR-CAD), was developed during the pandemic to improve COVID-19 case finding and triage. In high burden TB countries, the use of highly portable CXR and computer aided detection software has been adopted more broadly to improve the screening and triage of individuals for TB, but there is little evidence in these settings regarding COVID-19 CAD performance. We performed a multicenter, retrospective cross-over study evaluating CXRs from individuals at risk for COVID-19.
View Article and Find Full Text PDFPLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFBr J Radiol
January 2025
Joint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada.
Objectives: To evaluate 18F-DCFPyL-PET/MRI whole-gland-derived radiomics for detecting clinically significant (cs) prostate cancer (PCa) and predicting metastasis.
Methods: Therapy-naïve PCa patients who underwent 18F-DCFPyL PET/MRI were included. Whole-prostate-segmentation was performed.
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