A radiation dose survey has been undertaken involving 256 patients to investigate the dosimetric impact of breast tomosynthesis screening by employing different breast densities estimated by the Dance model, 50-50 breast model, and patient-specific density software: Volpara. Mean glandular dose (MGD) based on the Dance model provided the most realistic dose estimate with an average difference of -3.3 ± 4.8% from the patient-specific estimation. Average differences of -8.2 ± 6.5% and -7.3 ± 4.7% were observed for the 50-50 breast model and console MGD, respectively. We conclude that the Dance model should be used for dose calculations in radiation dose surveys and establishing diagnostic reference levels (DRL).
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/tbj.13209 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The analysis of dance teaching system in deep residual network fusing gated recurrent unit based on artificial intelligence.
Sci Rep
January 2025
Academy of Music, Suihua University, Suihua, 152000, China.
The purpose of this study is to investigate how deep learning and other artificial intelligence (AI) technologies can be used to enhance the intelligent level of dance instruction. The study develops a dance action recognition and feedback model based on the Graph Attention Mechanism (GA) and Bidirectional Gated Recurrent Unit (3D-Resnet-BigRu). In this model, time series features are captured using BiGRU after 3D-ResNet is inserted to extract video features.
View Article and Find Full Text PDFExplainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions.
Sci Rep
January 2025
School of Dance and Martial Arts, Capital University of Physical Education and Sports, Beijing, 100191, China.
How to utilize modern technological means to provide both accurate scoring and objective feedback for martial arts movements has become an issue that needs to be addressed in the field of physical education. This study proposes an intelligent scoring method based on machine learning. Firstly, the key features are extracted by the feature alignment technique, which eliminates the influence of athletes' movement speed, rhythm and duration on the scoring, thus reflecting the athletes' skill level more realistically.
View Article and Find Full Text PDFFrom theory to practice: modeling performance in breaking.
Front Sports Act Living
December 2024
Univ. Rouen-Normandie, Laboratoire Centre d'Études des Transformations des Activités Physiques et Sportives (CETAPS-UR 3832), Mont-Saint-Aignan, France.
A dance movement quality evaluation model using transformer encoder and convolutional neural network.
Sci Rep
December 2024
School of Literature, Law and Art, East China University of Technology, Nanchang, 330013, China.
The purpose of this study is to put forward a new evaluation model of dance movement quality to deal with the subjectivity and inconsistency in traditional evaluation methods. In view of the complexity and diversity of dance art and the widespread popularity of dance videos on social media, it is particularly urgent to develop an automatic and efficient tool for evaluating the quality of dance movements. Therefore, this study puts forward the Transformer Convolutional Neural Network with Dynamic and Static Streams (TransCNN-DSSS) model, which combines the analysis of dynamic flow and static flow, and makes use of the advantages of Transformer and Convolutional Neural Network (CNN) to deeply analyze and evaluate the dance movements.
View Article and Find Full Text PDFA Multilayer Network Model for Motor Competence from the View of the Science of Complexity.
Sports Med
December 2024
Research Centre in Physical Activity, Health and Leisure, and Laboratory for Integrative and Translational Research in Population Health, University of Porto, Porto, Portugal.
Motor competence is related to a large number of correlates of different natures, forming together a system with flexible parts that are synergically and cooperatively connected to produce a wide range of motor outcomes that cannot be explained from a predetermined linear view or a unique mechanism. The diversity of interacting correlates, the various connections between them, and the fast changes between assessments at different time points are clear barriers to the study of motor competence. In this manuscript, we present a multilayer framework that accounts for the theoretical background and the potential mathematical procedures necessary to represent the non-linear, complex, and dynamic relationships between several underlying correlates that emerge as a motor competence network.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!