The development of new solutions in craniofacial surgery brings the need to increase the accuracy of 3D printing models. The accuracy of the manufactured models is most often verified using optical coordinate measuring systems. However, so far, no decision has been taken regarding which type of system would allow for a reliable estimation of the geometrical accuracy of the anatomical models. Three types of optical measurement systems (Atos III Triple Scan, articulated arm (MCA-II) with a laser head (MMD × 100), and Benchtop CT160Xi) were used to verify the accuracy of 12 polymer anatomical models of the left side of the mandible. The models were manufactured using fused deposition modeling (FDM), melted and extruded modeling (MEM), and fused filament fabrication (FFF) techniques. The obtained results indicate that the Atos III Triple Scan allows for the most accurate estimation of errors in model manufacturing. Using the FDM technique obtained the best accuracy in models manufactured (0.008 ± 0.118 mm for ABS0-M30 and 0.016 ± 0.178 mm for PC-10 material). A very similar value of the standard deviation of PLA and PET material was observed (about 0.180 mm). The worst results were observed in the MEM technique (0.012 mm ± 0.308 mm). The knowledge regarding the precisely evaluated errors in manufactured models within the mandibular area will help in the controlled preparation of templates regarding the expected accuracy of surgical operations.
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http://dx.doi.org/10.3390/polym13142271 | DOI Listing |
JCO Clin Cancer Inform
January 2025
Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.
View Article and Find Full Text PDFOrthod Craniofac Res
January 2025
Sleep Unit, Department of Stomatology, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain.
Objectives: This non-randomised clinical study aimed to identify the phenotypic characteristics that distinguish responders from non-responders. Additionally, it sought to establish a predictive model for treatment response to obstructive sleep apnoea (OSA) using mandibular advancement devices (MAD), based on the analysed phenotypic characteristics.
Material And Methods: This study, registered under identifier NCT05596825, prospectively analysed MAD treatment over 6 years using two-piece adjustable appliances according to a standardised protocol.
Arch Orthop Trauma Surg
January 2025
Department of Orthopaedics, Wright State University, 30 E Apple St., Suite 2200, Dayton, OH, 45409, USA.
Introduction: We propose and assess the biomechanical stability of medial column screw supplementation in a synthetic distal femur fracture model.
Materials And Methods: Twenty-four low density synthetic femora modeling osteoporotic, intraarticular distal femur fractures with medial metaphyseal comminution were split into two fixation groups: (1) lateral locking distal femur plate (PA- plate alone) and (2) lateral locking distal femur plate with a 6.5 mm fully threaded medial cannulated screw (PWS- plate with screw).
Bioengineering (Basel)
January 2025
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
Recent advancements in deep learning have significantly improved medical image segmentation. However, the generalization performance and potential risks of data-driven models remain insufficiently validated. Specifically, unrealistic segmentation predictions deviating from actual anatomical structures, known as a Seg-Hallucination, often occur in deep learning-based models.
View Article and Find Full Text PDFBioengineering (Basel)
January 2025
School of Computer Science and Engineering, Sun-Yat sen University, Guanghzou 510006, China.
The consistency regularization method is a widely used semi-supervised method that uses regularization terms constructed from unlabeled data to improve model performance. Poor-quality target predictions in regularization terms produce noisy gradient flows during training, resulting in a degradation in model performance. Recent semi-supervised methods usually filter out low-confidence target predictions to alleviate this problem, but also prevent the model from learning features from unlabeled data in low-confidence regions.
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