Computer-aided recognition of dental implants in X-ray images

Abstract

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential.In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported.Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant's manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size.Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.

Artificial intelligence applications in implant dentistry: A systematic review

“…The AI models used among the different studies are presented in Table 3. The selected articles were distributed into 3 groups depending on the application of the AI model: implant type recognition (Supplementary Table 1, available online), 13,[27][28][29][30][31][32] models to determine osteointegration success or implant success prediction by using patient risk factors and ontology criteria (Supplementary Table 2, available online), [33][34][35][36][37][38][39] and implant design optimization by combining FEA calculations and AI models (Supplementary Table 2, available online). [40][41][42] The overall accuracy outcome of the AI models developed in the different reviewed studies ranged from 93.8% to 98%.…”

“…[40][41][42] The overall accuracy outcome of the AI models developed in the different reviewed studies ranged from 93.8% to 98%. 13,[27][28][29][30][31][32] The AI models to predict osteointegration or implant success by using different input data varied among the studies ranging from 62.4% to 80.5%. [33][34][35][36][37][38][39] Finally, the studies that developed AI models to optimize implant designs seem to agree on the applicability of AI models to improve implant designs, minimizing the stress at the implant-bone interface by 36.6% compared with the FEA model, 40 optimizing the implant design porosity, length, and diameter, improving the FEA calculations, 41 or accurately determining the elastic modulus of the implantbone interface.…”

“…Optimization implant design Implant type recognition The JBI Critical Appraisal Checklist for Quasi-Experimental results showed a 100% low risk of bias in all included articles for question 8. For question 1, all the studies obtained a low risk of bias except for that of Morais et al 13 that obtained a high risk of bias. For question 4, all the investigations attained a low risk of bias except for those of Hadj Saïd et al, 27 Papantonopoulos et al, 36 Zaw et al, 42 Sukegawa et al, 29 Ha et al, 34 Morais et al, 13 Takahashi et al, 32 and Kim et al 30 that showed a high risk of bias.…”

“…10,11 Different AI models have been developed for image recognition of the implant type by using periapical and panoramic radiographs. 12,13 Furthermore, AI models have also used dental radiographs to diagnose different lesions such as periodontal disease [14][15][16][17] or dental caries. [18][19][20][21][22][23] Similarly, AI applications have been reported for developing prediction models to determine osteointegration success or implant prognosis by using patient risk factors and ontology criteria, as well as optimizing dental implant designs by combining finite element analysis (FEA) calculations and AI models.…”

Current technology for identifying dental implants: a narrative review

“…According to Morais et al (2015), a dental implant recognition novel computer-aided framework was suggested. They used this method for a segmentation strategy for semi-automatic implant delineation and a machine learning approach for the recognition of an implant model design.…”