RESUMO
Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1-96.7%; AUC 0.944-0.970) and upper anterior (86.7-90.2%; 0.948-0.958) and lower (85.6-87.2%; 0.913-0.937) and upper posterior teeth (78.1-81.0%; 0.851-0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed.
RESUMO
Interest in machine learning models and convolutional neural networks (CNNs) for diagnostic purposes is steadily increasing in dentistry. Here, CNNs can potentially help in the classification of periodontal bone loss (PBL). In this study, the diagnostic performance of five CNNs in detecting PBL on periapical radiographs was analyzed. A set of anonymized periapical radiographs (N = 21,819) was evaluated by a group of trained and calibrated dentists and classified into radiographs without PBL or with mild, moderate, or severe PBL. Five CNNs were trained over five epochs. Statistically, diagnostic performance was analyzed using accuracy (ACC), sensitivity (SE), specificity (SP), and area under the receiver operating curve (AUC). Here, overall ACC ranged from 82.0% to 84.8%, SE 88.8-90.7%, SP 66.2-71.2%, and AUC 0.884-0.913, indicating similar diagnostic performance of the five CNNs. Furthermore, performance differences were evident in the individual sextant groups. Here, the highest values were found for the mandibular anterior teeth (ACC 94.9-96.0%) and the lowest values for the maxillary posterior teeth (78.0-80.7%). It can be concluded that automatic assessment of PBL seems to be possible, but that diagnostic accuracy varies depending on the location in the dentition. Future research is needed to improve performance for all tooth groups.
RESUMO
(1) Background: Caries, periapical lesions, periodontal bone loss (PBL), and endo-perio lesions are common dental findings that require an accurate diagnostic assessment to allow appropriate disease management. The purpose of this reliability study was to compare the inter- and intra-rater reliability for the detection of the above-mentioned pathologies on periapical radiographs. (2) Methods: Fourteen dentists (three with more than two years and eleven with less than two years of work experience) participated in a training workshop prior to data acquisition. A total of 150 radiographs were assessed by all raters in two rounds. Cohen's Kappa (CK) values and a binary logistic regression were calculated. (3) Results: The reliability was found in a moderate and substantial range of agreement: caries (mean inter-rater CK value/first round 0.704/mean inter-rater CK value/second round 0.659/mean intra-rater CK value 0.778), periapical lesions (0.643/0.611/0.768), PBL (0.454/0.482/0.739) and endo-perio lesion (0.702/0.689/0.840). The regression model revealed a significant influence of the clinical experience, and furthermore, periapical pathologies and PBL were identified less reliably in comparison to caries and endo-perio lesions. (4) Conclusions: The dentist's ability to detect the chosen pathologies was linked with significant differences. Periapical lesions and PBL were identified less reliably than caries and endo-perio lesions.
RESUMO
(1) Background: This in vitro reliability study aimed to determine the inter- and intra-examiner reliability for the detection of direct fillings, indirect crown restorations, root canal fillings and implants on periapical radiographs. (2) Methods: Fourteen dentists (<2 years of clinical experience = 11; >2 years of clinical experience = 3) participated in this diagnostic reliability study in which included a theoretical and practical educational training prior to data collection. The image set of periapical radiographs (N = 150) was examined in two evaluation rounds by all the dentists. Cohen's Kappa (CK) and a binary logistic regression model were computed. (3) Results: The inter- and intra-examiner reliability were found to be in almost perfect agreement: direct fillings (1st round 0.859/2nd round 0.844/intra 0.910), indirect crown restorations (0.932/0.926/0.955), root canal fillings (0.920/0.886/0.941) and dental implants (0.994/0.988/0.987). The binary logistic regression model revealed that the "evaluation round" and "dentist's clinical experience" had no significant influence, but for the "diagnostic category"; small, but statistically significant differences were documented. (4) Conclusions: The reliability for detecting direct and indirect restorations, root canal fillings or implants on periapical radiographs was found to be high in the present reliability study on periapical radiographs.