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Automated Identification of Dental Implants Using Artificial Intelligence

2021; Quintessence Publishing Company; Volume: 36; Issue: 5 Linguagem: Inglês

10.11607/jomi.8684

1942-4434

Rafael Pereira da Mata Santos, Higor Prado, Idalísio Neto, Guilherme Alves de Oliveira, A.F.A. Silva, Élton Gonçalves Zenóbio, Flávio Ricardo Manzi,

Medical Imaging and Analysis

Rafael Pereira da Mata Santos, DDS, MSc/Higor Eduardo Vieira Oliveira Prado, BA/Idalísio Soares Aranha Neto, PhD/Guilherme Augusto Alves de Oliveira, PhD/Amaro Ilídio Vespasiano Silva, PhD/Elton Gonçalves Zenóbio, PhD/Flávio Ricardo Manzi, PhD: Purpose: To develop and evaluate the accuracy of a computer-assisted system based on artificial intelligence for detecting and identifying dental implant brands using digital periapical radiographs. Materials and Methods: A total of 1,800 digital periapical radiographs of dental implants from three distinct manufacturers (f1 = 600, f2 = 600, and f3 = 600) were split into training dataset (n = 1,440 [80%]) and testing dataset (n = 360 [20%]) groups. The images were evaluated by software developed by means of convolutional neural networks (CNN), with the aim of identifying the manufacturer of the dental implants contained in them. Accuracy, sensitivity, specificity, positive and negative predictive values, and the receiver operating characteristic (ROC) curve were calculated for detection and diagnostic performance of the CNN algorithm. Results: At the final epoch (25), system accuracy values of 99.78% were obtained for group training data, 99.36% for group testing data, and 85.29% for validation data. The latter value corresponded to the actual accuracy of carrying out the system learning process. Conclusion: This study demonstrated the effectiveness of CNN for identifying dental implant manufacturers, which was proven to be a precise method of great clinical significance.