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RESEARCH
Year : 2023  |  Volume : 23  |  Issue : 1  |  Page : 84-89

Using deep learning approaches for coloring silicone maxillofacial prostheses: A comparison of two approaches


1 Department of Prosthodontics, Faculty of Dentistry, Gazi University, Ankara, Turkey
2 Department of Computer Engineering, Faculty of Engineering, Atilim University, Ankara, Turkey
3 Department of Computer Engineering, Faculty of Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey

Correspondence Address:
Meral Kurt
Department of Prosthodontics, Faculty of Dentistry, Gazi University, Ankara 06510
Turkey
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jips.jips_149_22

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Aim: This study aimed to compare the performance of two deep learning algorithms, attention-based gated recurrent unit (GRU), and the artificial neural networks (ANNs) algorithm for coloring silicone maxillofacial prostheses. Settings and Design: This was an in vitro study. Materials and Methods: A total of 21 silicone samples in different colors were produced with four pigments (white, yellow, red, and blue). The color of the samples was measured with a spectrophotometer, then the L*, a*, and b* values were recorded. The relationship between the L*, a*, and b* values of each sample and the amount of each pigment in the compound of the same sample was used as the training dataset, entered into each algorithm, and the prediction models were obtained. While generating the prediction model for each sample, the data of the corresponding sample assigned as the target color were excluded. L*, a*, and b* values of each target sample were entered into the obtained models separately, and recipes indicating the ratios for mixing the four pigments were predicted. The mean absolute error (MAE) and root mean square error (RMSE) values between the original recipe used in the production of each silicone and the recipe created by both prediction models for the same silicone were calculated. Statistical Analysis Used: Data were analyzed with the Student t-test (α=0.05). Results: The mean RMSE values and MAE values for the ANN algorithm (0.029 ± 0.0152 and 0.045 ± 0.0235, respectively) were found significantly higher than the attention-based GRU model (0.001 ± 0.0005 and 0.002 ± 0.0008, respectively) (P < 0.001). Conclusions: Attention-based GRU model provided better performance than the ANN algorithm with respect to the MAE and RMSE values.


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