Development of image analysis using Python: Relationship between matrix ratio of composite resin and curing temperature

Abstract

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).

Analysis of Dental Material Components Using Cosine Similarity

“…Therefore, in this experiment, we first replicated existing similarity analyses on metal samples, and then experimented with bovine teeth to see if similarity analysis was possible for inorganic materials. The results showed that for the metal sample (12% Au-Ag-Pd alloy), a match of >99% with the manufacturer's declared values was detected, consistent with previous reports 4) . For alloys other than the 12% Au-Ag-Pd alloy, a lower similarity of around 50% was detected, proving, as in previous reports, that the identification of samples using cosine similarity search is extremely effective.…”

“…The WDS results were used as is, without normalization. To calculate the cosine similarity between the analysis results for the alloy ingot surface and the standard values (Table 2, 12% Au-Ag-Pd alloy), we used the program reported by Hori et al 4) and computed the mean and standard deviation. Similarly, we performed cosine similarity searches between the analysis data for the bovine teeth and their standard values.…”

“…X-ray microanalysis (XMA) is the standard method for elemental analysis in such cases, but objectively evaluating the results is not always straightforward. Therefore, Hori et al 4) developed a program that applies cosine similarity search, a method used in artificial intelligence learning algorithms, to determine the origin of samples by calculating the degree of similarity between the elemental analysis results of specimens and standard values. In the present study, we performed a quantitative analysis of dental alloys, which are representative metal-based samples, and bovine teeth as natural inorganic samples.…”