Forming models and brazing parts, both of which require high accuracy, are greatly affected the polymerization shrinkage of pattern resin. In 2018, a lower-shrinkage autopolymerizing pattern resin (PRK) was introduced. In this work, we compared the rate of polymerization shrinkage between PRK with that of three autopolymerizing resins -GC Pattern Resin (GPR), Pi-Ku Plast (PIK), and Fixpeed (FIX)-as controls. The shrinkage percentages at 10 min were 7.26±0.88 for PRK, 10.78±2.28 for GPR, 8.03±1.08% for PIK, and 7.46±1.25 for FIX. The shrinkage of PRK was significantly lower than that of GPR. The lower-shrinkage autopolymerizing resin contains some multifunctional monomer and indicated that the amount of monomer was accordingly reduced from the result of polymer size and abundance ratio. Our results suggested that the monomer component and the polymer particle size were factors that contribute to reducing contraction of the resins.

Most studies of artificial intelligence in the medical field involve classification problems, but few consider recognition of one characteristic point in images or regression analysis such as data recognition. In this research, we constructed a fundamental convolutional neural network framework for regression analysis. Images of the handwritten digit "3" from the MNIST dataset were used as training data, with the protruding middle point as an image feature point. Input images and training data (x1, y1) were connected to 6 convolutional layers and then run through 2 affine layers to produce the output data (x2, y2). The loss function was the mean radial error (MRE) between the training and output data. After machine learning, the error converged to 0.75 pixels on average. We expect that this algorithm can be clinically applied to points having certain characteristics in images, such as locating hard tissue lesions or recognizing measurement points in cephalograms.

The aim of this study was to establish a measurement method for filler and matrix in cured resin composite (RC) using Python programming and to investigate the correlation between matrix ratio and curing temperature rise. Eight kinds of RCs were used. Backscattered electron images were taken for each cured specimen. Matrix and filler contents were calculated using Python programming with the K-means or area segmentation method. Volume measurement methods were assessed for comparison. Heat released during the polymerization reaction was measured. The matrix ratio was calculated without human intervention. Three specimens contained only inorganic filler, and other specimens contained multiple types of fillers. Almost the same values of the matrix ratio were obtained by programming and the volume measurement methods for specimens containing a single type of inorganic filler. Moreover, a strong correlation was found between the matrix ratio obtained by the programming method and curing temperature rise (R=0.9826).

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Although dental evidence is frequently used for the identification of unidentified persons, information about the many types of alloys used in prosthetics is not utilized. If the type of alloy can be identified from a small amount of material, the scope of the search could be narrowed. In this experiment, a method was investigated for identifying the alloy type using 3 kinds of cutting points (a white point and 2 types of silicone points). Wavelength-dispersive X-ray spectroscopy (WDS) was used for elemental analysis. The elements were translated into multidimensional vectors, and the cosine similarity was calculated to compare vectors of the WDS results and vectors of the official data of alloys. According to the results, cosine similarity showed a concordance of more than 0.8. The developed program is expected to be useful as a method for identifying alloy types using only a small amount of grinding dust.