Automated deep learning for classification of dental implant radiographs using a large multi-center dataset

Abstract

This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surgery were collected from five college dental hospitals and 10 private dental clinics, and validated by the National Information Society Agency and the Korean Academy of Oral and Maxillofacial Implantology. The dataset contained a total of 156,965 panoramic and periapical radiographic images and comprised 10 manufacturers and 27 different types of DIS. The accuracy, precision, recall, F1 score, and confusion matrix were calculated to evaluate the classification performance of the automated DL algorithm. The performance metrics of the automated DL based on accuracy, precision, recall, and F1 score for 116,756 panoramic and 40,209 periapical radiographic images were 88.53%, 85.70%, 82.30%, and 84.00%, respectively. Using only panoramic images, the DL algorithm achieved 87.89% accuracy, 85.20% precision, 81.10% recall, and 83.10% F1 score, whereas the corresponding values using only periapical images achieved 86.87% accuracy, 84.40% precision, 81.70% recall, and 83.00% F1 score, respectively. Within the study limitations, automated DL shows a reliable classification accuracy based on large-scale and comprehensive datasets. Moreover, we observed no statistically significant difference in accuracy performance between the panoramic and periapical images. The clinical feasibility of the automated DL algorithm requires further confirmation using additional clinical datasets.

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]).…”

“…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]). Each of them shared common funding sources and grant numbers, respectively, but the studies by Kong et al [31,61] also shared a common research registration number.…”

“…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.…”

“…More than 431,000 implant images were used to train and test the selected AI tools' implant detection and classification accuracy. 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 sources and grant numbers, respectively, but the studies by Kong et al [31,61] also shared a common research registration number. The number of algorithm networks evaluated for accuracy varied in the selected studies.…”

“…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.…”