Purpose: This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. Materials and Methods: A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. Results: The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals. Conclusion: Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

Purpose: This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. Materials and Methods: A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. Results: The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals. Conclusion: Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

Purpose: This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. Materials and Methods: A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. Results: The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals. Conclusion: Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

Introduction: Race estimation of unknown individual is essential in forensic investigation. The resiliency of frontal sinus makes it a potential tool for biological profiling, particularly in cases where fragmented skeleton persists. Geometric morphometrics is an efficient way to characterise shape. However, the use of frontal sinus to identify race of Malaysians is yet to be investigated. This research employed a two-dimensional (2D) geometric morphometric to examine the morphological differences of the frontal sinus among the major races in Malaysia. Methods: Lateral skull radiographs which comprising of 453 adult Malaysian (151 Malays, Chinese and Indian respectively) were used. The 2D landmarks of eight were placed on the digitalized radiographs and 2D geometric morphometric analysis was performed using MorphoJ software. Results: Procrustes ANOVA revealed a significantly different frontal sinus shape (p-value < 0.05) between races. Canonical variate analysis showed significantly different frontal sinus morphology (p-value < 0.05) between Malay and Indian as well as Chinese and Indian. Discriminant function analysis with cross-validation demonstrated a 57.4% accuracy rate. Conclusion: This population-specific study based on frontal sinus of Malaysians using the 2D geometric morphometric, though less reliable, sheds new light on the potential applicability of this method for race estimation purpose.

Purpose: The objective of this study was to determine the minimum number of teeth in the anterior dental arch that would yield accurate results for individual identification in forensic contexts. Materials and Methods: The study involved the analysis of 28 sets of 3-dimensional (3D) point cloud data, focused on the labial surface of the anterior teeth. These datasets were superimposed within each group in both genuine and imposter pairs. Group A incorporated data from the right to the left central incisor, group B from the right to the left lateral incisor, and group C from the right to the left canine. A comprehensive analysis was conducted, including the evaluation of root mean square error (RMSE) values and the distances resulting from the superimposition of dental arch segments. All analyses were conducted using CloudCompare version 2.12.4 (Telecom ParisTech and R&D, Kyiv, Ukraine). Results: The distances between genuine pairs in groups A, B, and C displayed an average range of 0.153 to 0.184 mm. In contrast, distances for imposter pairs ranged from 0.338 to 0.522 mm. RMSE values for genuine pairs showed an average range of 0.166 to 0.177, whereas those for imposter pairs ranged from 0.424 to 0.638. A statistically significant difference was observed between the distances of genuine and imposter pairs (P<0.05). Conclusion The exceptional performance observed for the labial surfaces of anterior teeth underscores their potential as a dependable criterion for accurate 3D dental identification. This was achieved by assessing a minimum of 4 teeth.

Frontal sinus has unique anatomical features that are distinct to every population. However, the distribution of frontal sinus patterns has yet to be explored in the Malaysian population. This study aimed to describe the distribution of frontal sinus patterns among adult Malaysians. 409 adult Malaysian posteroanterior skull radiographs, consisting of 200 males and 209 females of Malay, Chinese, and Indian races aged between 20–69 years old, were included in the study. The frontal sinus patterns were classified according to total and percentage of presence or absence of frontal sinus, symmetry or asymmetrical (right or left dominant), unilateral absence (right or left), bilateral absence, and lobulation. The findings showed that bilateral presence of frontal sinus is common, in 95.4% of individuals and bilateral absence was noted in 2.7% individuals. Unilateral absence was found in 2.0% of individuals. Asymmetrical frontal sinus was observed in 54.5% of population meanwhile 40.8% showed symmetrical frontal sinus. The majority of individuals, regardless of sex, race, and age, possessed 1 to 3 lobes on both sides of the frontal sinus. The findings suggest that the frontal sinus is highly asymmetric, and the absence of the frontal sinus is rare. This morphological variation provides an insight into the landmarking placement for measurement during forensic application and assists neurosurgeons in surgical procedure to avoid breaching of the frontal sinus.

The modiolus of the face manifests the interesting landmark for facial muscles attachment. The strong connective tissue fibres play an important role in the clinical setting, especially in the aesthetic and dental surgeries. In the fourth week of intrauterine life, the development of the modiolus evolves in accordance with the growth of muscles of facial expression. Microscopically, a white, tendinous structure with the thick irregular collagenous connective tissue of collagen fibres predominance appeared to be the modiolus. Modiolus is morphologically a fibromuscular muscle situated on the lateral border of the mouth. The formation of the nasolabial fold is important and a well-developed modiolus provide a toned face. Several works of literature forementioned the number of facial muscles attached to the modiolus but no definitive similarity are identified. This review summarizes the updated morphological features and applied anatomy of the facial modiolus with its muscle attachment.

Adult ; Classification ; Diagnosis ; Humans ; Malaysia ; Malocclusion ; Passive Cutaneous Anaphylaxis ; Retrospective Studies

In the article, two co-authors were missing in the author list.