AIM: To develop an artificial neural network for vertical root fracture detection. MATERIALS AND METHODS: A probabilistic neural network design was used to clarify whether a tooth root was sound or had a vertical root fracture. Two hundred images (50 sound and 150 vertical root fractures) derived from digital radiography--used to train and test the artificial neural network--were divided into three groups according to the number of training and test data sets: 80/120,105/95 and 130/70, respectively. Either training or tested data were evaluated using grey-scale data per line passing through the root. These data were normalized to reduce the grey-scale variance and fed as input data of the neural network. The variance of function in recognition data was calculated between 0 and 1 to select the best performance of neural network. The performance of the neural network was evaluated using a diagnostic test. RESULTS: After testing data under several variances of function, we found the highest sensitivity (98%), specificity (90.5%) and accuracy (95.7%) occurred in Group three, for which the variance of function in recognition data was between 0.025 and 0.005. CONCLUSIONS: The neural network designed in this study has sufficient sensitivity, specificity and accuracy to be a model for vertical root fracture detection.
AIM: To develop an artificial neural network for vertical root fracture detection. MATERIALS AND METHODS: A probabilistic neural network design was used to clarify whether a tooth root was sound or had a vertical root fracture. Two hundred images (50 sound and 150 vertical root fractures) derived from digital radiography--used to train and test the artificial neural network--were divided into three groups according to the number of training and test data sets: 80/120,105/95 and 130/70, respectively. Either training or tested data were evaluated using grey-scale data per line passing through the root. These data were normalized to reduce the grey-scale variance and fed as input data of the neural network. The variance of function in recognition data was calculated between 0 and 1 to select the best performance of neural network. The performance of the neural network was evaluated using a diagnostic test. RESULTS: After testing data under several variances of function, we found the highest sensitivity (98%), specificity (90.5%) and accuracy (95.7%) occurred in Group three, for which the variance of function in recognition data was between 0.025 and 0.005. CONCLUSIONS: The neural network designed in this study has sufficient sensitivity, specificity and accuracy to be a model for vertical root fracture detection.
Authors: Shankargouda Patil; Sarah Albogami; Jagadish Hosmani; Sheetal Mujoo; Mona Awad Kamil; Manawar Ahmad Mansour; Hina Naim Abdul; Shilpa Bhandi; Shiek S S J Ahmed Journal: Diagnostics (Basel) Date: 2022-04-19