The mental foramen is present on either side of the body of the mandible bone. This foramen transmits mental vessels and nerves. In forensic anthropology, mental foramen may be important for differentiating sex, estimating age and identifying various races based on morphology. The main aim of the present study was to determine the position, shape and diameter of the mental foramen according to sex, age and race by postmortem computed tomography in the Malaysian population. Materials and Methods: A total of 79 dentulous patients (48 males, 31 females) from 3 age groups (18-30 years, 31– 50 years, 51-74 years) were selected for this study, and ten parameters were observed for each mandible. The parameters were divided into two morphological and eight morphometric parameters. The morphometric parameters were measured by using Osirix MD Software 3D Volume Rendering. Results: Results showed that mandibular body length and height were significantly greater in males than in females by independent t-test. (p< 0.05). However, the mandibular body height was found to decrease significantly with age in both sexes by one-way Anova. It was observed that the shape of mental foramen was 45.6% oval and 54.4% rounded. About 44.3% of them were in line with the longitudinal axis of the second premolar tooth. Conclusion: It was concluded that mental foramen may be used for identification purposes, particularly for sex, age and race determination.

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.

Sex determination is one of the main steps in the identification of human skeletal remains. It constitutes an initial step in personal identification from the skeletal remains. The aim of the present study was to provide the population-specific sex discriminating osteometric standards to aid human identification. The present study was conducted on 87 (174 sides) slices of crania using postmortem computed tomography in 45 males and 42 females, aged between 18 and 75 years. About 22 parameters of crania were measured using Osirix software 3-D Volume Rendering. Results showed that all parameters were significantly higher in males than in females except for orbital height of the left eye by independent t test (P<0.01). By discriminant analysis, the classification accuracy was 85.1%, and by regression, the classification accuracy ranged from 78.2% to 86.2%. In conclusion, cranium can be used to distinguish between males and females in the Malaysian population. The results of the present study can be used as a forensic tool for identification of unknown crania.
Classification ; Female ; Forensic Anthropology ; Humans ; Male ; Orbit ; Skull ; Tomography, X-Ray Computed

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

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

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.

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.