Objectives: For this research two different ways for integrating intra-oral scans into three-dimensional (3D) stereophotogrammetric images are analyzed and compared to the gold standard method.
Materials And Methods: A cross-sectional study was performed. For each patient a complete dataset was collected, which was used to generate 3D fusion models by three different methods: method A using cheek retractors, method B using a tracer and method C using full-skull CBCT. The experimental methods A and B were compared to the gold standard method C.
Results: A group of eighteen patients were included in this study. The translation (X, Y,Z), the euclidean distance and the rotation (roll, pitch, yaw) were calculated for both experimental methods A and B in comparison with the gold standard method C. Twelve out of fourteen measurements were clinically acceptable (below 2 mm or 2 degrees). Method A shows the highest deviation in the pitch-orientation for rotation (2.51 degrees, 95% CI [1.756 … 3.272]), while method B shows a higher deviation along the y-axis (1.85 mm, 95% CI [1.224 … 2.467]).
Conclusions: This study shows promising results of non-ionizing methods to integrate intra-oral scans into 3D stereophotogrammetric images. With improved accuracy in pitch in method A and translation along the Y-axis in method B, all measurements will be within the clinically acceptable threshold. However, since these two measurements exceed the clinically acceptable thresholds, the complete model positioning is less accurate. Therefore the main goal in further research should be to improve the accuracy of the pitch in method A and the translation along the Y-axis in method B. Additionally, for clinical use the biggest improvement could be gained by optimizing the clinical workflow and data processing.
Clinical Relevance: By using a non-ionizing 3D fusion model instead of a conventional cephalogram for treatment planning, the ionizing dose during orthodontic treatment can be significantly reduced.
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http://dx.doi.org/10.1007/s00784-024-06138-8 | DOI Listing |
Skelet Muscle
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Department of Molecular Physiology and Biophysics, and Department of Neurology, Howard Hughes Medical Institute, Senator Paul D. Wellstone Muscular Dystrophy Specialized Research Center, Roy J. and Lucille A. Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA.
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View Article and Find Full Text PDFPlant Methods
January 2025
School of Electronic and Information Engineering, Liaoning Technical University, Huludao, 125105, China.
Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No.37, Guoxue Lane, Wuhou District, Chengdu, China.
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View Article and Find Full Text PDFBMC Public Health
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School of Nursing, Tung Wah College, 16/F, Ma Kam Chan Memorial Building, 31 Wylie Road, Kowloon, Hong Kong SAR, People's Republic of China.
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View Article and Find Full Text PDFArthritis Res Ther
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Scientific Research Center, China-Japan Union Hospital of Jilin University, Changchun, Jilin, 130033, China.
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