A system for designing removable partial dentures using artificial intelligence. Part 1. Classification of partially edentulous arches using a convolutional neural network

Abstract

Purpose: The purpose of this study was to develop a method for classifying dental arches using a convolutional neural network (CNN) as the first step in a system for designing removable partial dentures. Methods: Using 1184 images of dental arches (maxilla: 748 images; mandible: 436 images), arches were classified into four arch types: edentulous, intact dentition, arches with posterior tooth loss, and arches with bounded edentulous space. A CNN method to classify images was developed using Tensorflow and Keras deep learning libraries. After completion of the learning procedure, the diagnostic accuracy, precision, recall, F-measure and area under the curve (AUC) for each jaw were calculated for diagnostic performance of learning. The classification was also predicted using other images, and percentages of correct predictions (PCPs) were calculated. The PCPs were compared with the Kruskal-Wallis test (p = 0.05). Results: The diagnostic accuracy was 99.5% for the maxilla and 99.7% for the mandible. The precision, recall, and F-measure for both jaws were 0.25, 1.0 and 0.4, respectively. The AUC was 0.99 for the maxilla and 0.98 for the mandible. The PCPs of the classifications were more than 95% for all types of dental arch. There were no significant differences among the four types of dental arches in the mandible. Conclusions:The results of this study suggest that dental arches can be classified and predicted using a CNN. Future development of systems for designing removable partial dentures will be made possible using this and other AI technologies.

Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review

“…The included studies were ranged from the year 2000 to 2020. The studies were from four categories: cohort study [ 5 , 16 , 27 ], case control [ 28 , 29 ], clinical trials [ 6 – 12 , 17 , 30 – 43 ], and experimental trials [ 13 – 15 , 44 , 45 ]. The follow-up period was mentioned in one study [ 43 ].…”

“…The follow-up period was mentioned in one study [ 43 ]. The various techniques of artificial intelligence was applied in the field oral and maxillofacial surgery [ 27 – 29 , 38 , 45 ], oral medicine [ 28 ], oral radiology [ 42 ], esthetic dentistry [ 17 , 44 ], restorative dentistry [ 11 – 13 , 34 , 36 , 40 ], endodontics [ 6 , 9 , 10 , 41 ], oral diagnosis [ 5 – 9 , 15 , 33 , 34 , 36 , 37 , 42 , 43 , 45 ], orthodontics and orthognathic surgery [ 16 , 35 ], forensic dentistry [ 8 ], gerodontology [ 39 ], implantology [ 43 ], periodontics [ 31 , 32 ], and prosthodontics [ 14 , 15 , 30 ].…”

“…The dropout rate was mentioned in 31 studies [ 5 – 11 , 13 – 17 , 27 – 45 ]. The study variables were analyzed for accuracy in 30 studies [ 5 – 13 , 15 – 17 , 27 – 29 , 37 , 39 – 45 ]. Sample size was mentioned in 31 studies [ 5 – 17 , 27 – 33 , 35 – 45 ].…”

“…According to the standards described in the Cochrane Handbook for Systematic Reviews of Interventions (v5.1.0) [19], the following findings were recorded. Out of the 32 studies [5][6][7][8][9][10][11][12][13][14][15][16][17][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] assessed, 1 study employed blinding [6]. In 5 studies, randomizations [5,7,34,37,39] were performed.…”

“…Furthermore, an automated robotic system can fulfill the requirements of typical dental operations with accurate, safe, and three-dimensional (3D) tooth preparation [ 13 , 14 ]. The AI convolutional neural network (CNN) can be utilized for classifying dental arches and designing removable partial dentures [ 15 ]. AI can analyze the impact of orthognathic treatment on facial attractiveness and age appearance.…”