THIRD MOLAR MATURITY INDEX IN INDONESIAN JUVENILES: COMPARING LINEAR AND POLYNOMIAL KERNEL PERFORMANCE IN SUPPORT VECTOR REGRESSION FOR DENTAL AGE ESTIMATION

Dental age estimation is a branch of forensic odontology that plays a pivotal role in identifying, examining, or determining the legal status of the living and the dead. This research explores the capability of support vector regression to estimate chronological age from the third molar maturity index (I3M) in Indonesian Juveniles and compares the linear and kernel performance. Two hundred and twenty-two orthopantomo-graphy were measured using I3M in the lower left third molar and processed using R Studio with Caret extension. The analysis was separated into two groups, group 1 using only I3M as a predictor, and group 2 using both I3M and sex. Both groups were analyzed using SVR with the linear and polynomial kernel. The result suggests that using polynomial kernel SVR in group 1 produces the best results, with an R2 value of 0.64, RMSE of 1.588 years, and MAE of 1.25 years using degree = 3, c = 0.25. However, the addition of a sex predictor in the model reduces its accuracy when using the polynomial kernel

Dental age estimation in Indonesian adults:An investigation of the maxillary canine pulp-to-tooth volume ratio using cone-beam computed tomography

Purpose: This study was performed to develop a linear regression model using the pulp-to-tooth volume ratio (PTVR) ratio of the maxillary canine, assessed through cone-beam computed tomography (CBCT) images, to predict chronological age (CA) in Indonesian adults.Materials and Methods: A sample of 99 maxillary canines was collected from patients between 20 and 49.99 years old. These samples were obtained from CBCT scans taken at the Universitas Padjadjaran Dental Hospital in Indonesia between 2018 and 2022. Pulp volume (PV) and tooth volume (TV) were measured using ITK-SNAP, while PTVR was calculated from the PV/TV ratio. Using RStudio, a linear regression was performed to predict CA using PTVR. Additionally, correlation and observer agreement were assessed.Results: The PTVR method demonstrated excellent reproducibility, and a significant correlation was found between the PTVR of the maxillary canine and CA (r=-0.74, P&lt;0.01). The linear regression analysis showed an R2 of 0.58, a root mean square error of 5.85, and a mean absolute error of 4.31.Conclusion: Linear regression using the PTVR can be effectively applied to predict CA in Indonesian adults between 20 and 49.99 years of age. As models of this type can be population-specific, recalibration for each population is encouraged. Additionally, future research should explore the use of other teeth, such as molars.</p

Machine Learning Assisted 5-Part Tooth Segmentation Method for CBCT-Based Dental Age Estimation in Adults

Background: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG5t) method. Additionally, supervised machine learning modelling —namely support vector regression (SVR) with linear and polynomial kernel, and regression tree — was tested and compared with the multiple linear regression model.Material and Methods: CBCT scans from 99 patients aged between 20 to 59.99 were collected. Eighty eligible teeth including maxillary canine, lateral incisor, and central incisor were used in this study.  Enamel to dentine volume ratio, pulp to dentine volume ratio, lower tooth volume ratio, and sex was utilized as independent variable to predict chronological age. Results: No multicollinearity was detected in the models. The best performing model comes from maxillary lateral incisor using SVR with polynomial kernel (R2adj = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean absolute error and 6.05 years of root means squared error. However, SG5t demands a complex approach to segment the enamel volume in the crown section and a lengthier labor time of 45 minutes per tooth

Machine Learning Assisted 5-Part Tooth Segmentation Method for CBCT-Based Dental Age Estimation in Adults

Background: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG5t) method. Additionally, supervised machine learning modelling —namely support vector regression (SVR) with linear and polynomial kernel, and regression tree — was tested and compared with the multiple linear regression model.Material and Methods: CBCT scans from 99 patients aged between 20 to 59.99 were collected. Eighty eligible teeth including maxillary canine, lateral incisor, and central incisor were used in this study.  Enamel to dentine volume ratio, pulp to dentine volume ratio, lower tooth volume ratio, and sex was utilized as independent variable to predict chronological age. Results: No multicollinearity was detected in the models. The best performing model comes from maxillary lateral incisor using SVR with polynomial kernel (R2adj = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean absolute error and 6.05 years of root means squared error. However, SG5t demands a complex approach to segment the enamel volume in the crown section and a lengthier labor time of 45 minutes per tooth

Towards fully automated third molar development staging in panoramic radiographs

Staging third molar development is commonly used for age assessment in sub-adults. Current staging techniques are, at most, semi-automated and rely on manual interactions prone to operator variability. The aim of this study was to fully automate the staging process by employing the full potential of deep learning, using convolutional neural networks (CNNs) in every step of the procedure. The dataset used to train the CNNs consisted of 400 panoramic radiographs (OPGs), with 20 OPGs per developmental stage per sex, staged in consensus between three observers. The concepts of transfer learning, using pre-trained CNNs, and data augmentation were used to mitigate the issues when dealing with a limited dataset. In this work, a three-step procedure was proposed and the results were validated using fivefold cross-validation. First, a CNN localized the geometrical center of the lower left third molar, around which a square region of interest (ROI) was extracted. Second, another CNN segmented the third molar within the ROI. Third, a final CNN used both the ROI and the segmentation to classify the third molar into its developmental stage. The geometrical center of the third molar was found with an average Euclidean distance of 63 pixels. Third molars were segmented with an average Dice score of 93%. Finally, the developmental stages were classified with an accuracy of 54%, a mean absolute error of 0.69 stages, and a linear weighted Cohen’s kappa coefficient of 0.79. The entire automated workflow on average took 2.72 s to compute, which is substantially faster than manual staging starting from the OPG. Taking into account the limited dataset size, this pilot study shows that the proposed fully automated approach shows promising results compared with manual staging.Internal Funds KU Leuvenhttp://link.springer.com/journal/4142021-04-01hj2020Anatom

Sixty years of research in dental age estimation: a bibliometric study

Abstract Background Dental age estimation (DAE) research has grown rapidly and became one of the biggest topics in forensic odontology. This study aimed to evaluate the DAE research trends over the span of 60 years using bibliometric analysis. Methods Sampling was performed in the Scopus database using a search string (“Dental Age Estimation” OR “Age Determination by Teeth”) to detect DAE-related studies. The search was performed from inception to the year 2022. A data-cleaning intervention using a fuzzy-matching technique was done to unify the author and affiliation name variations. Results The initial search returned 1638 articles, years of publication ranging from 1964 to 2022, with an approximate growth rate of 5.9% a year. Source analysis showed that most of the top article sources were Forensic Science International (n = 200). Cameriere R presents the overall highest score (77 articles, Local h-index 30). Authors from Shanghai Jiao Tong University produced the highest number of publications (n = 111). The most locally cited study was “A New System of Dental Age Assessment” by Demirjian et al. (Hum Biol 45:211-227, 1973) (n = 1507). The trending topics analysis shows that earlier DAE studies were focused on dental regressive changes and later changed focus to utilizing technological advancements. Institutions and Author's collaborations were also found to be internationally diverse with 20.82% of the articles being a product of international co-authorships. Conclusions DAE research has grown rapidly helped by multiple advancements in various technological ends. Along with the high demand for DAE analysis, authors and publishers need to continually improve their standards for their respective research and reporting and continue to increase collaboration