Accurate detection for dental implant and peri-implant tissue by transfer learning of faster R-CNN: a diagnostic accuracy study

Abstract

Background The diagnosis of dental implants and the periapical tissues using periapical radiographs is crucial. Recently, artificial intelligence has shown a rapid advancement in the field of radiographic imaging. Purpose This study attempted to detect dental implants and peri-implant tissues by using a deep learning method known as object detection on the implant image of periapical radiographs. Methods After implant treatment, the periapical images were collected and data were processed by labeling the dental implant and peri-implant tissue together in the images. Next, 300 images of the periapical radiographs were split into 80:20 ratio (i.e. 80% of the data were used for training the model while 20% were used for testing the model). These were evaluated using an object detection model known as Faster R-CNN, which simultaneously performs classification and localization. This model was evaluated on the classification performance using metrics, including precision, recall, and F1 score. Additionally, in order to assess the localization performance, an evaluation through intersection over union (IoU) was utilized, and, Average Precision (AP) was used to assess both the classification and localization performance. Results Considering the classification performance, precision = 0.977, recall = 0.992, and F1 score = 0.984 were derived. The indicator of localization was derived as mean IoU = 0.907. On the other hand, considering the indicators of both classification and localization performance, AP showed an object detection level of AP@0.5 = 0.996 and AP@0.75 = 0.967. Conclusion Thus, the implementation of Faster R-CNN model for object detection on 300 periapical radiographic images including dental implants, resulted in high-quality object detection for dental implants and peri-implant tissues.

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.…”

“…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.…”

Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review

“…It accurately classified and localised dental implants and peri‐implant tissues. It proved a proper clinical tool for evaluating the resorption level of the peri‐implant marginal bone and identifying unknown implants on periapical radiographs [78]. Table 4 shows details of the studies that have used AI‐based models for the detection of bone loss in implantology.…”