Wiadomosci lekarskie (Warsaw, Poland : 1960) · 2020
Application of neural network technologies in the dental caries forecast.
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Udod Oleksandr A, Voronina Hanna S, Ivchenkova Olena Yu
Аннотация
OBJECTIVE: The aim: of the work was to develop and apply in the clinical trial a software product for the dental caries prediction based on neural network programming.
PATIENTS AND METHODS: Materials and methods: Dental examination of 73 persons aged 6-7, 12-15 and 35-44 years was carried out. The data obtained during the survey were used as input for the construction and training of the neural network. The output index was determined by the increase in the intensity of caries, taking into account the number of cavities. To build a neural network, a high-level Python programming language with the NumPay extension was used.
RESULTS: Results: The intensity of carious dental lesions was the highest in 35-44 years old patients - 6.69 ± 0.38, in 6-7 years old children and 12-15 years old children it was 3.85 ± 0.27 and 2.15 ± 0.24, respectively (p <0.05). After constructing and training the neural network, 61 true and 12 false predictions were obtained based on these indices, the accuracy of predicting the occurrence of caries was 83.56%. Based on these results, a graphical user interface for the "CariesPro" software application was created.
CONCLUSION: Conclusions: The resulting neural network and the software product based on it permit to predict the development of dental caries in persons of all ages with a probability of 83.56%.
PATIENTS AND METHODS: Materials and methods: Dental examination of 73 persons aged 6-7, 12-15 and 35-44 years was carried out. The data obtained during the survey were used as input for the construction and training of the neural network. The output index was determined by the increase in the intensity of caries, taking into account the number of cavities. To build a neural network, a high-level Python programming language with the NumPay extension was used.
RESULTS: Results: The intensity of carious dental lesions was the highest in 35-44 years old patients - 6.69 ± 0.38, in 6-7 years old children and 12-15 years old children it was 3.85 ± 0.27 and 2.15 ± 0.24, respectively (p <0.05). After constructing and training the neural network, 61 true and 12 false predictions were obtained based on these indices, the accuracy of predicting the occurrence of caries was 83.56%. Based on these results, a graphical user interface for the "CariesPro" software application was created.
CONCLUSION: Conclusions: The resulting neural network and the software product based on it permit to predict the development of dental caries in persons of all ages with a probability of 83.56%.