Deep learning-based dental implant recognition using synthetic X-ray images

Abstract

A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of different angles) of 3D volumetric representations of computer-aided design (CAD) surface models. A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. However, as part of the fully automated system, suitable ROIs are automatically detected. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging. Graphical abstract

Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review

“…All the involved studies were published in the last four years (2020: six; 2021: four; 2022: five; 2023: six) (Figure 2). Out of selected 21 studies, 12 were conducted in the Republic of Korea [31,48,51,[53][54][55][57][58][59][60][61][62], four in Japan [30,47,50,52], and one each in Brazil [49], India [56], France [46], South Africa [32], and the United States [63] (Figure 3). Some of the included studies were conducted by the same research groups (Kong et al [31,61], Park et al [48,62], Sukegawa et al [30,50,52], and Lee et al [51,53,54]).…”

“…The number of algorithm networks evaluated for accuracy varied in the selected studies. Ten studies [46][47][48][49]51,53,57,58,60,62] evaluated the accuracy of one algorithm network; three evaluated two algorithm networks [32,59,61]; two tested three algorithm networks [31,54]; one tested four algorithm networks [56]; three tested five algorithm networks [50,52,55]; one study each tested six [30] and ten [63] algorithm networks. All the included studies evaluated the accuracy of tested AI tools in implant detection and classification, whereas four studies [51,53,60,62] also compared this to trained dental professionals.…”

“…Eight studies [30,31,47,50,52,56,60,61] used cropped panoramic X-ray images, and six studies [49,55,[57][58][59]63] used cropped periapical X-ray images, whereas another six studies [46,48,51,53,54,62] used both periapical and panoramic implant images. In one study [32], artificially generated X-ray images were used to test AI accuracy. In most of the selected studies, the test group to training group ratio was 1:4.…”

“…The training data are generally from the pool of collected data of interest [14][15][16][17]. Currently, AI is widely used in dentistry, which involves caries detection [18,19], periapical lesion detection [20], oral cancer diagnosis [21,22], screening of osteoporosis [23], working length determination during endodontic treatment [24,25], determination of root morphology [26,27], forensic odontology [28], pediatric dentistry [29], and implant dentistry for identification [30][31][32], diagnosis, and treatment planning [33,34]. Studies have shown that, in general, AI helps dentists in diagnosis and treatment planning, as it provides logical reasons that aid in scientific assessment.…”

“…AI is reported to have a high success rate in medical image classification and is effectively used in this area. AI has been used to manage the problem of implant system identification and classification [30][31][32][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63]. The AI tool is trained using a database of implant images and is later used to identify and classify the implants.…”