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Development of predictive algorithms for the wear resistance of denture teeth materials
CONCLUSIONS: 3D printed denture teeth materials showed the lowest wear out of all studied for 48 months simulation. LSTM model was successfully developed to predict wear of various denture teeth. The developed LSTM model has the potential to reduce simulation duration and specimen number for wear testing of various dental materials, while potentially improving the accuracy and reliability of wear testing predictions. This work paves the way for generalized multi-sample models enhanced with empirical information.PMID:37392604 | DOI:10.1016/j.jmbbm.2023.105984
Source: Journal of the Mechanical Behavior of Biomedical Materials - July 1, 2023 Category: Materials Science Authors: Anastasiia Grymak Mei Ting Tieh Alexander Hui Xiang Yang Joanne Jung Eun Choi Source Type: research

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies
ConclusionsDL-based, comprehensive SSAT, DSAT, and VAT analysis tool showed high accuracy and reproducibility and provided a comprehensive fat compartment composition analysis and visualization in less than 10  s.
Source: Magnetic Resonance Materials in Physics, Biology and Medicine - August 2, 2021 Category: Materials Science Source Type: research