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Age estimation in forensic odontology is mainly based on the development of permanent teeth. To register the developmental status of an examined tooth, staging techniques were developed. However, due to inappropriate calibration, uncertainties during stage allocation, and lack of experience, non-uniformity in stage allocation exists between expert observers. As a consequence, related age estimation results are inconsistent. An automated staging technique applicable to all tooth types can overcome this drawback.This study aimed to establish an integrated automated technique to stage the development of all mandibular tooth types and to compare their staging performances.Calibrated observers staged FDI teeth 31, 33, 34, 37 and 38 according to a ten-stage modified Demirjian staging technique. According to a standardised bounding box around each examined tooth, the retrospectively collected panoramic radiographs were cropped using Photoshop CC 2021® software (Adobe®, version 23.0). A gold standard set of 1639 radiographs were selected (n 31  = 259, n 33  = 282, n 34  = 308, n 37  = 390, n 38  = 400) and input into a convolutional neural network (CNN) trained for optimal staging accuracy. The performance evaluation of the network was conducted in a five-fold cross-validation scheme. In each fold, the entire dataset was split into a training and a test set in a non-overlapping fashion between the folds (i.e., 80% and 20% of the dataset, respectively). Staging performances were calculated per tooth type and overall (accuracy, mean absolute difference, linearly weighted Cohen's Kappa and intra-class correlation coefficient). Overall, these metrics equalled 0.53, 0.71, 0.71, and 0.89, respectively. All staging performance indices were best for 37 and worst for 31. The highest number of misclassified stages were associated to adjacent stages. Most misclassifications were observed in all available stages of 31.Our findings suggest that the developmental status of mandibular molars can be taken into account in an automated approach for age estimation, while taking incisors into account may hinder age estimation., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Dental age estimation, a cornerstone in forensic age assessment, has been extensively tried and tested, yet manual methods are impeded by tedium and interobserver variability. Automated approaches using deep transfer learning encounter challenges like data scarcity, suboptimal training, and fine-tuning complexities, necessitating robust training methods. This study explores the impact of convolutional neural network hyperparameters, model complexity, training batch size, and sample quantity on age estimation. EfficientNet-B4, DenseNet-201, and MobileNet V3 models underwent cross-validation on a dataset of 3896 orthopantomograms (OPGs) with batch sizes escalating from 10 to 160 in a doubling progression, as well as random subsets of this training dataset. Results demonstrate the EfficientNet-B4 model, trained on the complete dataset with a batch size of 160, as the top performer with a mean absolute error of 0.562 years on the test set, notably surpassing the MAE of 1.01 at a batch size of 10. Increasing batch size consistently improved performance for EfficientNet-B4 and DenseNet-201, whereas MobileNet V3 performance peaked at batch size 40. Similar trends emerged in training with reduced sample sizes, though they were outperformed by the complete models. This underscores the critical role of hyperparameter optimization in adopting deep learning for age estimation from complete OPGs. The findings not only highlight the nuanced interplay of hyperparameters and performance but also underscore the potential for accurate age estimation models through optimization. This study contributes to advancing the application of deep learning in forensic age estimation, emphasizing the significance of tailored training methodologies for optimal outcomes., (© 2024 American Academy of Forensic Sciences.)

Objective: To compare conventional radiography (CR) and magnetic resonance imaging (MRI) of the left hand/wrist and both clavicles for forensic age estimation of adolescents and young adults., Materials and Methods: CR and MRI were prospectively conducted in 108 healthy Caucasian volunteers (52 males, 56 females) aged 16 to 21 years. Skeletal development was assessed by allocating stages (wrist, clavicles) and atlas standards (hand/wrist). Inter- and intra-observer agreements were quantified using linear weighted Cohen's kappa, and descriptive statistics regarding within-stage/standard age distributions were reported., Results: Inter- and intra-observer agreements for hand/wrist CR (staging technique: 0.840-0.871 and 0.877-0.897, respectively; atlas method: 0.636-0.947 and 0.853-0.987, respectively) and MRI (staging technique: 0.890-0.932 and 0.897-0.952, respectively; atlas method: 0.854-0.941 and 0.775-0.978, respectively) were rather similar. The CR atlas method was less reproducible than the staging technique. Inter- and intra-observer agreements for clavicle CR (0.590-0.643 and 0.656-0.770, respectively) were lower than those for MRI (0.844-0.852 and 0.866-0.931, respectively). Furthermore, although shifted, wrist CR and MRI within-stage age distribution spread were similar, as were those between staging techniques and atlas methods. The possibility to apply (profound) substages to clavicle MRI rendered a more gradual increase of age distributions with increasing stages, compared to CR., Conclusions: For age estimation based on the left hand/wrist and both clavicles, reference data should be considered anatomical structure- and imaging modality-specific. Moreover, CR is adequate for hand/wrist evaluation and a wrist staging technique seems to be more useful than an atlas method. By contrast, MRI is of added value for clavicle evaluation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Increasing cross-border migration has brought forensic age assessment into focus in recent decades. Forensic age estimation is based on the three pillars: physical and medical constitution, bone age, and tooth age. Part of the bone age examination includes the assessment of the medial end of the clavicles when the hand bones are already fully developed and a minority must be excluded. Recent research has brought MRI to the forefront as a radiation-free alternative for age assessment. However, there exits only a few studies with large sample size regarding the clavicles and with controversies about staging, motion artifacts, and exclusion based on anatomic norm variants. In the current prospective study, 338 central European male individuals between 13 and 24 years of age underwent MRI examination of the sternoclavicular region. Development was assessed by three blinded raters according to the staging system described by Schmeling et al. and Kellinghaus et al. and related to age by descriptive statistics and transition analyses with a cumulative probit model. In addition, reliability calculations were performed. No statistically significant developmental difference was found between the left and right clavicles. Inter-rater agreement was only moderate, but intra-rater agreement, on the other hand, was good. Stage 3c had a minimum age of 19.36 years and appears to be a good indicator of proof of majority. The minimum age of stage 4 was lower compared with other studies, 20.18 years, and therefore seems not to be an indicator of age of 21 years. In conclusion, we confirmed the value of clavicular MRI in the age estimation process. The transition analysis model is a good approach to circumvent the problems of age mimicry and samples that are not fully equilibrated. Given the moderate agreement between raters, a consensus reading is recommended., (© 2022. The Author(s).)

The current study aimed to select the best mandibular morphological identifiers. One-hundred eighty-five panoramic radiographs were retrospectively collected, in which four landmarks were located on the mandible: the most superior point of the condyle right/left (CONR/L), of the coronoid right/left (CORR/L), of the mandibular lingula right/left (LINR/L), and the most mesial point of the mental foramen right/left (MMFR/L). Five linear measurements, 6 angles, and 10 ratios were measured bilaterally. Three groups of statistics were considered: (1) mean potential set; (2) inter-observer agreement quantified by intra-class correlation (ICC) and within-subject coefficient of variation (WSCV); and (3) Spearman correlation. Parameters were selected for a step-by-step cascade. In a univariate approach, the following parameters proved to have the best identifying capacity: ratio 3 right (between lines CONR - CORR and LINR - MMFR) with mean potential set 13%, ICC 0.90, and WSCV 4.8%; ratio 4 (between lines CONR/L - CORR/L and MMFR - MMFL) with mean potential set 13%, ICC 0.92, and WSCV 8.9%; and angle 4 left (between landmarks LINL, MMFL, and MMFR) with mean potential set of 18%, ICC 0.91, and WSCV 1.2%. The correlation coefficients ranged from 0.01 to 0.33. In a multivariate approach, the identifying capacity improved drastically, with all ratios combined as the strongest identifier (mean potential set 1.29%). In conclusion, a single ratio or a single angle already narrows down the set of potential matches, but the mean potential set remains relatively large. Combining all ratios drastically increases the certainty of the match., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The current study aimed to select the best dental morphological identifiers for human identification. Sixty-two panoramic radiographs were collected retrospectively, in which six measurements were performed on all seven mandibular left permanent teeth: tooth length (TL), crown length (CL), root length (RL), crown width (CW), cervical width (CEJW), and root width (RW). Nine length-width ratios were then calculated using these measurements. Three groups of statistics were considered: (1) inter-observer reliability quantified by intra-class correlation (ICC); (2) mean "potential set"; and (3) Spearman correlation. A step-by-step cascade was then established based on selected parameters. In a univariate approach, the following parameters were the best identifiers: TL/CW for tooth 36 (ICC 0.82; mean potential set 13.7%), TL/CEJW for tooth 35 (ICC 0.87; mean potential set 15.2%), and TL/RW for tooth 32 (ICC 0.89; mean potential set 16.0%). The correlations between these three parameters ranged from 0.24 to 0.47. In a multivariate approach, the following parameters had the best identifying capacity: all parameters combined for tooth 31 (mean potential set 8.1%), for tooth 35 (mean potential set 11.9%), and for tooth 32 (mean potential set 16.3%). In conclusion, a single ratio in a specific tooth narrows down the potential set of matches, but the mean potential set remains relatively large. Combining all ratios of a single specific tooth increases the certainty of the match. In particular, tooth 31 was the strongest identifier., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: To evaluate the effect of third molar impaction and impaction-related parameters on third molar development., Materials and Methods: Panoramic radiographs (N=3972) from 473 males and 558 females between 3.2 and 23.5 years old were analysed. Three parameters of impaction were examined: hindering contact between third and adjacent second molar, retromolar space availability (only in lower third molars), and angulation between the third and adjacent second molar. From the separate parameters, a definition for impaction was derived. Third molars' development was staged according to a modified Köhler et al. staging technique. A linear model was used to compare within-stage and overall age, as a function of hindering contact, retromolar space, and impaction. Furthermore, a quadratic function was used to study the correlation between age and angulation., Results: Significant differences were found in mean age as a function of hindering contact and retromolar space, depending on third molar location and stage. There was a significant relation between angulation and age, depending on the stage, with all third molars evolving to a more upright position (closer to 0°). Mean ages of subjects with impacted third molars were significantly lower in certain third molar stages, but the differences were clinically small (absolute differences ≤0.65 years). Moreover, after correction for stage differences, no significant differences in age could be demonstrated., Conclusions: The development of impacted and non-impacted third molars can be considered clinically equal in our study population., Clinical Relevance: There is no distinction required between impacted and non-impacted third molars for dental age estimation., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The use of MRI in forensic age estimation has been explored extensively during the last decade. The authors of this paper synthesized the available MRI data for forensic age estimation in living children and young adults to provide a comprehensive overview that can guide age estimation practice and future research. To do so, the authors searched MEDLINE, Embase and Web of Science, along with cited and citing articles and study registers. Two authors independently selected articles, conducted data extraction, and assessed risk of bias. They considered study populations including living subjects up to 30 years old. Fifty-five studies were included in qualitative analysis and 33 in quantitative analysis. Most studies had biases including use of relatively small European (Caucasian) populations, varying MR approaches and varying staging techniques. Therefore, it was not appropriate to pool the age distribution data. The authors found that reproducibility of staging was remarkably lower in clavicles than in any other anatomical structure. Age estimation performance was in line with the gold standard, radiography, with mean absolute errors ranging from 0.85 years to 2.0 years. The proportion of correctly classified minors ranged from 65% to 91%. Multifactorial age estimation performed better than that based on a single anatomical site. The authors found that more multifactorial age estimation studies are necessary, together with studies testing whether the MRI data can safely be pooled. The current review results can guide future studies, help medical professionals to decide on the preferred approach for specific cases, and help judicial professionals to interpret the evidential value of age estimation results.

Medical imaging for forensic age estimation in living adolescents and young adults continues to be controversial and a subject of discussion. Because age estimation based on medical imaging is well studied, it is the current gold standard. However, large disparities exist between the centers conducting age estimation, both between and within countries. This review provides an overview of the most common approaches applied in Europe, with case examples illustrating the differences in imaging modalities, in staging of development, and in statistical processing of the age data. Additionally, the review looks toward the future because several European research groups have intensified studies on age estimation, exploring four strategies for optimization: (1) increasing sample sizes of the reference populations, (2) combining single-site information into multifactorial information, (3) avoiding ionizing radiation, and (4) conducting a fully automated analysis., Competing Interests: None declared., (Thieme. All rights reserved.)

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.

Objectives: This feasibility study aimed to investigate the reliability of multi-factorial age estimation based on MR data of the hand, wisdom teeth and the clavicles with reduced acquisition time., Methods: The raw MR data of 34 volunteers-acquired on a 3T system and using acquisition times (T A ) of 3:46 min (hand), 5:29 min (clavicles) and 10:46 min (teeth)-were retrospectively undersampled applying the commercially available CAIPIRINHA technique. Automatic and radiological age estimation methods were applied to the original image data as well as undersampled data to investigate the reliability of age estimates with decreasing acquisition time. Reliability was investigated determining standard deviation (SSD) and mean (MSD) of signed differences, intra-class correlation (ICC) and by performing Bland-Altman analysis., Results: Automatic age estimation generally showed very high reliability (SSD < 0.90 years) even for very short acquisition times (SSD ≈ 0.20 years for a total T A of 4 min). Radiological age estimation provided highly reliable results for images of the hand (ICC ≥ 0.96) and the teeth (ICC ≥ 0.79) for short acquisition times (T A  = 16 s for the hand, T A  = 2:21 min for the teeth), imaging data of the clavicles allowed for moderate acceleration (T A  = 1:25 min, ICC ≥ 0.71)., Conclusions: The results demonstrate that reliable multi-factorial age estimation based on MRI of the hand, wisdom teeth and the clavicles can be performed using images acquired with a total acquisition time of 4 min.

Staging third molar development is commonly used for age estimation in subadults. Automated developmental stage allocation to the mandibular left third molar in panoramic radiographs has been examined in a pilot study. This method used an AlexNet Deep Convolutional Neural Network (CNN) approach to stage lower left third molars, which had been selected by manually drawn bounding boxes around them. This method (bounding box AlexNet = BA) still contained parts of surrounding structures which may have affected the automated stage allocation performance. We hypothesize that segmenting only the third molar could further improve the automated stage allocation performance. Therefore, the current study aimed to determine and validate the effect of lower third molar segmentations on automated tooth development staging. Retrospectively, 400 panoramic radiographs were collected, processed and segmented in three ways: bounding box (BB), rough (RS), and full (FS) tooth segmentation. A DenseNet201 CNN was used for automated stage allocation. Automated staging results were compared with reference stages - allocated by human observers - overall and per stage. FS rendered the best results with a stage allocation accuracy of 0.61, a mean absolute difference of 0.53 stages and a Cohen's linear κ of 0.84. Misallocated stages were mostly neighboring stages, and DenseNet201 rendered better results than AlexNet by increasing the percentage of correctly allocated stages by 3% (BA compared to BB). FS increased the percentage of correctly allocated stages by 7% compared to BB. In conclusion, full tooth segmentation and a DenseNet CNN optimize automated dental stage allocation for age estimation., (© 2019 American Academy of Forensic Sciences.)

Purpose: To determine how motion affects stage allocation to the clavicle's sternal end on MRI., Materials and Methods: Eighteen volunteers (9 females, 9 males) between 14 and 30 years old were prospectively scanned with 3-T MRI. One resting-state scan was followed by five intentional motion scans. Additionally, a control group of 72 resting-state scans were selected from previous research. Firstly, six observers allocated developmental stages to the clavicles independently. Secondly, they re-assessed the images, allocating developmental statuses (immature, mature). Finally, the resting-state scans of the 18 volunteers were assessed in consensus to decide on the "correct" stage/status. Results were compared between groups (control, prospective resting state, prospective motion), and between staging techniques (stages/statuses)., Results: Inter-observer agreement was low (Krippendorff α 0.23-0.67). The proportion of correctly allocated stages (64%) was lower than correctly allocated statuses (83%). Overall, intentional motion resulted in fewer assessable images and less images of sufficient evidential value. The proportion of correctly allocated stages did not differ between resting-state (64%) and motion scans (65%), while correctly allocated statuses were more prevalent in resting-state scans (83% versus 77%). Remarkably, motion scans did not render a systematically higher or lower stage/status, compared to the consensus., Conclusion: Intentional motion impedes clavicle MRI for age estimation. Still, in case of obvious disturbances, the forensic expert will consider the MRI unsuitable as evidence. Thus, the development of the clavicle as such and the staging technique seem to play a more important role in allocating a faulty stage for age estimation.

Background: MRI of the clavicle's sternal end has been studied for age estimation. Several pitfalls have been noted, but how they affect age estimation performance remains unclear., Purpose/hypothesis: To further study these pitfalls and to make suggestions for a proper use of clavicle MRI for forensic age estimation. Our hypotheses were that age estimation would benefit from 1) discarding stages 1 and 4/5; 2) including advanced substages 3aa, 3ab, and 3ac; 3) taking both clavicles into account; and 4) excluding morphological variants., Study Type: Prospective cross-sectional., Population: Healthy Caucasian volunteers between 11 and 30 years old (524; 277 females, 247 males)., Field Strength/sequence: 3T, T 1 -weighted gradient echo volumetric interpolated breath-hold examination (VIBE) MR-sequence., Assessment: Four observers applied the most elaborate staging technique for long bone development that has been described in the current literature (including stages, substages, and advanced substages). One of the observers repeated a random selection of the assessments in 110 participants after a 2-week interval. Furthermore, all observers documented morphological variants., Statistical Tests: Weighted kappa quantified reproducibility of staging. Bayes' rule was applied for age estimation with a continuation ratio model for the distribution of the stages. According to the hypotheses, different models were tested. Mean absolute error (MAE) differences between models were compared, as were MAEs between cases with and without morphological variants., Results: Weighted kappa equaled 0.82 for intraobserver and ranged between 0.60 and 0.64 for interobserver agreement. Stages 1 and 4/5 were allocated interchangeably in 4.3% (54/1258). Age increased steadily in advanced substages of stage 3, but improvement in age estimation was not significant (right P = 0.596; left P = 0.313). The model that included both clavicles and discarded stages 1 and 4/5 yielded an MAE of 1.97 years, a root mean squared error of 2.60 years, and 69% correctly classified minors. Morphological variants rendered significantly higher MAEs (right 3.84 years, P = 0.015; left 2.93 years, P = 0.022)., Data Conclusion: Our results confirmed hypotheses 3) and 4), while hypotheses 1) and 2) remain to be investigated in larger studies., Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:377-388., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: To study age estimation performance of combined magnetic resonance imaging (MRI) data of all four third molars, the left wrist and both clavicles in a reference population of females and males. To study the value of adding anthropometric and sexual maturation data., Materials and Methods: Three Tesla MRI of the three anatomical sites was prospectively conducted from March 2012 to May 2017 in 14- to 26-year-old healthy Caucasian volunteers (160 females, 138 males). Development was assessed by allocating stages, anthropometric measurements were taken, and self-reported sexual maturation data were collected. All data was incorporated in a continuation-ratio model to estimate age, applying Bayes' rule to calculate point and interval predictions. Two performance aspects were studied: (1) accuracy and uncertainty of the point prediction, and (2) diagnostic ability to discern minors from adults (≥18 years)., Results: Combining information from different anatomical sites decreased the mean absolute error (MAE) compared to incorporating only one site (P<0.0001). By contrast, adding anthropometric and sexual maturation data did not further improve MAE (P=0.11). In females, combining all three anatomical sites rendered a MAE equal to 1.41 years, a mean width of the 95% prediction intervals of 5.91 years, 93% correctly classified adults and 91% correctly classified minors. In males, the corresponding results were 1.36 years, 5.49 years, 94%, and 90%, respectively., Conclusion: All aspects of age estimation improve when multi-factorial MRI data of the three anatomical sites are incorporated. Anthropometric and sexual maturation data do not seem to add relevant information., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Objectives: Providing recommendations for wrist MRI in age estimation by determining (1) which anatomical structures to include in the statistical model, (2) which MRI sequence to conduct, and (3) which staging technique to apply., Methods: Radius and ulna were prospectively studied on 3 T MRI in 363 healthy Caucasian participants (185 females, 178 males) between 14 and 26 years old, using T1 spin echo (SE) and T1 gradient echo VIBE. Bone development was assessed applying a 5-stage staging technique with several amelioration attempts to optimise staging. A Bayesian model rendered point predictions of age and diagnostic indices to discern minors from adults., Results: All approaches rendered similar results, with none of them outperforming the others. A single bone assessment of radius or ulna sufficed. SE and VIBE sequences were both suitable, but needed sequence-specific age estimation. A one-fits-all 5-stage staging technique-with substages in stage 3-was suitable and did not benefit from profound substaging. Age estimation based on SE radius resulted in a mean absolute error of 1.79 years, a specificity (correctly identified minors) of 93%, and a discrimination slope of 0.640., Conclusion: Radius and ulna perform similarly to estimate age, and so do SE and VIBE. A one-fits-all staging technique can be applied., Key Points: • Radius and ulna perform similarly to estimate age. • SE and VIBE perform similarly, but age estimation should be based on the corresponding sequence-specific reference data. • A one-fits-all 5-stage staging technique with substages 3a, 3b, and 3c can be applied to both bones and both sequences.

Purpose: To compare the Ghent and Graz magnetic resonance imaging (MRI) protocols for third molars, focusing on the assessment of apical closure. To study the influence of (1) voxel size and (2) head fixation using a bite bar. To compare both protocols with a ground truth of apical development., Materials and Methods: In 11 healthy volunteers, 3T MRI was conducted, including four Ghent sequences and two Graz sequences, with and without bite bar. After removal, 39 third molars were scanned with 7T μMRI and μCT to establish the ground truth of apical development. Three observers in consensus evaluated assessability and allocated developmental stages., Results: The Ghent T2 FSE sequence (0.33 × 0.33 × 2 mm 3 ) was more assessable than the Graz T1 3D FSE sequence (0.59 × 0.59 × 1 mm 3 ). Comparing assessability in both sequences with bite bar rendered P = 0.02, whereas comparing those without bite bar rendered P < 0.001. Within the same sequence, the bite bar increased assessability, with P = 0.03 for the Ghent T2 FSE and P = 0.07 for the Graz T1 3D FSE. Considering μCT as ground truth for staging, allocated stages on MRI were most frequently equal or higher. Among in vivo protocols, the allocated stages did not differ significantly., Conclusion: Imaging modality-specific and MRI sequence-specific reference data are needed in age estimation. A higher in-plane resolution and a bite bar increase assessability of apical closure, whereas they do not affect stage allocation of assessable apices.

Aims: The present report describes an oral health promotion project in a residential facility for people with intellectual disabilities as a strategy to implement community service learning in undergraduate dental education., Methods and Results: Two undergraduate students developed a project aimed to improve the oral health in residents with intellectual disabilities by educating caregivers. The impact was investigated by a pretest-posttest design, evaluating residents' plaque and gingival indexes and caregivers' knowledge, behavior, attitude, and self-efficacy. Findings demonstrated that within a semester, it was possible for students to obtain a limited but positive impact on the local community., Discussion and Conclusion: Based on the experiences at Ghent University, the following guidelines were suggested to implement service learning in a dental curriculum that renders both a positive impact on student learning and the local community : (1) integrate community service learning in multiple years in the curriculum; (2) use a stepwise approach from theory to exercises to extramural experiences; (3) create a partnership with a local organization; (4) organize long-term projects and annual follow-up to increase the impact; (5) involve supervisors from both the university and the local organization; and (6) establish an evidence based approach to benefit the quality of projects and to facilitate students to develop research competences., (© 2018 Special Care Dentistry Association and Wiley Periodicals, Inc.)

Background: Automated methods to evaluate growth of hand and wrist bones on radiographs and magnetic resonance imaging have been developed. They can be applied to estimate age in children and subadults. Automated methods require the software to (1) recognise the region of interest in the image(s), (2) evaluate the degree of development and (3) correlate this to the age of the subject based on a reference population. For age estimation based on third molars an automated method for step (1) has been presented for 3D magnetic resonance imaging and is currently being optimised (Unterpirker et al. 2015)., Aim: To develop an automated method for step (2) based on lower third molars on panoramic radiographs., Materials and Methods: A modified Demirjian staging technique including ten developmental stages was developed. Twenty panoramic radiographs per stage per gender were retrospectively selected for FDI element 38. Two observers decided in consensus about the stages. When necessary, a third observer acted as a referee to establish the reference stage for the considered third molar. This set of radiographs was used as training data for machine learning algorithms for automated staging. First, image contrast settings were optimised to evaluate the third molar of interest and a rectangular bounding box was placed around it in a standardised way using Adobe Photoshop CC 2017 software. This bounding box indicated the region of interest for the next step. Second, several machine learning algorithms available in MATLAB R2017a software were applied for automated stage recognition. Third, the classification performance was evaluated in a 5-fold cross-validation scenario, using different validation metrics (accuracy, Rank-N recognition rate, mean absolute difference, linear kappa coefficient)., Results: Transfer Learning as a type of Deep Learning Convolutional Neural Network approach outperformed all other tested approaches. Mean accuracy equalled 0.51, mean absolute difference was 0.6 stages and mean linearly weighted kappa was 0.82., Conclusion: The overall performance of the presented automated pilot technique to stage lower third molar development on panoramic radiographs was similar to staging by human observers. It will be further optimised in future research, since it represents a necessary step to achieve a fully automated dental age estimation method, which to date is not available.

Background: The development of third molars can be evaluated with medical imaging to estimate age in subadults. The appearance of third molars on magnetic resonance imaging (MRI) differs greatly from that on radiographs. Therefore a specific staging technique is necessary to classify third molar development on MRI and to apply it for age estimation., Aim: To develop a specific staging technique to register third molar development on MRI and to evaluate its performance for age estimation in subadults., Materials and Methods: Using 3T MRI in three planes, all third molars were evaluated in 309 healthy Caucasian participants from 14 to 26 years old. According to the appearance of the developing third molars on MRI, descriptive criteria and schematic representations were established to define a specific staging technique. Two observers, with different levels of experience, staged all third molars independently with the developed technique. Intra- and inter-observer agreement were calculated. The data were imported in a Bayesian model for age estimation as described by Fieuws et al. (2016). This approach adequately handles correlation between age indicators and missing age indicators. It was used to calculate a point estimate and a prediction interval of the estimated age. Observed age minus predicted age was calculated, reflecting the error of the estimate., Results: One-hundred and sixty-six third molars were agenetic. Five percent (51/1096) of upper third molars and 7% (70/1044) of lower third molars were not assessable. Kappa for inter-observer agreement ranged from 0.76 to 0.80. For intra-observer agreement kappa ranged from 0.80 to 0.89. However, two stage differences between observers or between staging sessions occurred in up to 2.2% (20/899) of assessments, probably due to a learning effect. Using the Bayesian model for age estimation, a mean absolute error of 2.0 years in females and 1.7 years in males was obtained. Root mean squared error equalled 2.38 years and 2.06 years respectively. The performance to discern minors from adults was better for males than for females, with specificities of 96% and 73% respectively., Conclusion: Age estimations based on the proposed staging method for third molars on MRI showed comparable reproducibility and performance as the established methods based on radiographs.

Background: Established dental age estimation methods in sub-adults study the development of third molar root apices on radiographs. In living individuals, however, avoiding ionising radiation is expedient. Studying dental development with magnetic resonance imaging complies with this requirement, adding the advantage of imaging in three dimensions., Aim: To elaborate the development of an MRI protocol to visualise all third molars for forensic age estimation, with particular attention to the development of the root apex., Subjects and Methods: Ex vivo scans of porcine jaws and in vivo scans of 10 volunteers aged 17-25 years were performed to select adequate sequences. Studied parameters were T1 vs T2 weighting, ultrashort echo time (UTE), fat suppression, in plane resolution, slice thickness, 3D imaging, signal-to-noise ratio, and acquisition time. A bilateral four-channel flexible surface coil was used. Two observers evaluated the suitability of the images., Results: T2-weighted images were preferred to T1-weighted images. To clearly distinguish root apices in (almost) fully developed third molars an in plane resolution of 0.33 × 0.33 mm 2 was deemed necessary. Taking acquisition time limits into account, only a T2 FSE sequence with slice thickness of 2 mm generated images with sufficient resolution and contrast. UTE, thinner slice T2 FSE and T2 3D FSE sequences could not generate the desired resolution within 6.5 minutes., Conclusion: Three Tesla MRI of the third molars is a feasible technique for forensic age estimation, in which a T2 FSE sequence can provide the desired in plane resolution within a clinically acceptable acquisition time.

Background: Established methods to stage development of third molars for forensic age estimation are based on the evaluation of radiographs, which show a 2D projection. It has not been investigated whether these methods require any adjustments in order to apply them to stage third molars on magnetic resonance imaging (MRI), which shows 3D information., Aim: To prospectively study root stage assessment of third molars in age estimation using 3 Tesla MRI and to compare this with panoramic radiographs, in order to provide considerations for converting 2D staging into 3D staging and to determine the decisive root., Subjects and Methods: All third molars were evaluated in 52 healthy participants aged 14-26 years using MRI in three planes. Three staging methods were investigated by two observers. In sixteen of the participants, MRI findings were compared with findings on panoramic radiographs., Results: Decisive roots were palatal in upper third molars and distal in lower third molars. Fifty-seven per cent of upper third molars were not assessable on the radiograph, while 96.9% were on MRI. Upper third molars were more difficult to evaluate on radiographs than on MRI (p < .001). Lower third molars were equally assessable on both imaging techniques (93.8% MRI, 98.4% radiograph), with no difference in level of difficulty (p = .375). Inter- and intra-observer agreement for evaluation was higher in MRI than in radiographs. In both imaging techniques lower third molars showed greater inter- and intra-observer agreement compared to upper third molars. MR images in the sagittal plane proved to be essential for staging., Conclusion: In age estimation, 3T MRI of third molars could be valuable. Some considerations are, however, necessary to transfer known staging methods to this 3D technique.