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 (SG) 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 was 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 ( = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean average error and 6.05 years of root means squared error. However, demands a complex approach to segment the enamel volume in the crown section and a lengthier labour time of 45 minutes per tooth.

Identifying teeth and tooth fragments from digital 3D models.

Dental anatomy is an essential skill for human identification in forensic odontology. With the advent of technology enabling virtual autopsies, there is scope for virtual consultation by forensic odontologists especially when the expertise is unavailable but needed in zones of conflict or disasters. This study aimed to investigate potential benefits and challenges of identifying intact and damaged teeth from 3D scanned digital models. Ten 3D tooth models - nine permanent and deciduous human teeth and one animal tooth were uploaded on a hosting platform. A 3-part survey was circulated among 60 forensic odontologists with questions about demography (P1), tooth identification of the scanned 3D models (P2) and the perceived usefulness of 3D models for tooth identification (P3). This was the first time that a survey required the identification of individual human teeth (intact or not) and animal tooth combined. The response rate for study participation was 58%. Substantial agreement among participants was seen in the determination of tooth classification (i.e., molars, premolars) or non-human and tooth within the same tooth class (i.e., lateral incisors, second molar) (both k=0.61). The least agreement (k=0.21) was seen in identification of tooth according to the FDI notation with a mean accuracy of 0.34. While most responders correctly identified the animal tooth, most incorrect responses were seen in the identification of the intact third molar. While 3D-scanned teeth have the potential to be identified virtually, forensic odontologists should continuously test their skills in tooth morphology and dental anatomy of humans (damaged or not) and animals.

Radiographic morphology of canines tested for sexual dimorphism via convolutional-neural-network-based artificial intelligence.

The permanent left mandibular canines have been used for sexual dimorphism when human identification is necessary. Controversy remains whether the morphology of these teeth is actually useful to distinguish males and females. This study aimed to assess the sexual dimorphism of canines by means of a pioneering artificial intelligence approach to this end. A sample of 13,046 teeth radiographically registered from 5838 males and 7208 females between the ages of 6 and 22.99 years was collected. The images were annotated using Darwin V7 software. DenseNet121 was used and tested based on binary answers regarding the sex (male or female) of the individuals for 17 age categories of one year each (i.e. 6-6.99, 7.7.99… 22.22.99). Accuracy rates, receiver operating characteristic (ROC) curves and confusion matrices were used to quantify and express the artificial intelligence's classification performance. The accuracy rates across age categories were between 57-76% (mean: 68%±5%). The area under the curve (AUC) of the ROC analysis was between 0.58 and 0.77. The best performances were observed around the age of 12 years, while the worst were around the age of 7 years. The morphological analysis of canines for sex estimation should be restricted and allowed in practice only when other sources of dimorphic anatomic features are not available.

Applying artificial intelligence to determination of legal age of majority from radiographic data.

Forensic odontologists use biological patterns to estimate chronological age for the judicial system. The age of majority is a legally significant period with a limited set of reliable oral landmarks. Currently, experts rely on the questionable development of third molars to assess whether litigants can be prosecuted as legal adults. Identification of new and novel patterns may illuminate features more dependably indicative of chronological age, which have, until now, remained unseen. Unfortunately, biased perceptions and limited cognitive capacity compromise the ability of researchers to notice new patterns. The present study demonstrates how artificial intelligence can break through identification barriers and generate new estimation modalities. A convolutional neural network was trained with 4003 panoramic-radiographs to sort subjects into 'under-18' and 'over-18' age categories. The resultant architecture identified legal adults with a high predictive accuracy equally balanced between precision, specificity and recall. Moving forward, AI-based methods could improve courtroom efficiency, stand as automated assessment methods and contribute to our understanding of biological ageing.

Dental age estimation methods applied to Indian children and adolescents: A systematic review and meta-analysis.

Country-specific systematic reviews and meta-analyses have been proposed to compile the available literature and rank methods based on their performance for a target population. India is a country with a vast scientific literature on dental age estimation. This systematic review aimed to provide evidence to help the decision of experts regarding the method of choice for dental age estimation in India. The research protocol was registered in Open Science Framework. Literature Search was performed in Embase, LILACS, MedLine (via PubMed), SciELO, Scopus and Web of Science. Grey Literature was searched in Google Scholar and ProQuest. Observational cross-sectional studies that compared chronological and estimated ages using Demirjian (original [DEM] and Chaillet's modification [modified-DEM]) and Acharya (ACH) methods were included. JBI tool was used to assess the risk of bias. The search detected 9799 studies, out of which 56 were eligible (n=13,107 panoramic radiographs of Indian individuals). Low risk of bias was registered for 48 studies, while 8 presented a moderate risk of bias. The meta-analysis showed a standardized mean difference between chronological and estimated ages of -0.11 (95%CI: -0.29; 0.07), 0.74 (95%CI: 0.39; 1.09), and -0.01 (95%CI: -0.23; 0.22) years for DEM, modified-DEM and ACH, respectively. High heterogeneity (I2=88-93%) was observed across studies for all the methods, including subgroup analyses based on sex. This study ranked ACH, DEM and modified-DEM (from the best to the worse) performances in the Indian population.

Intersection of forensic odontology and psychology.

Forensic odontologists are expected to deal with challenging demands which can affect their mental health while dealing with forensic activities. This study aimed to explore the psychological impacts of forensic activities on forensic odontologists and students undertaking training. Firstly, it of an integrative review (part I) on the psychological effects of forensic odontology practice. The review was performed on Scopus, Medline and Web of Science. Next, an anonymous online survey using JISC Online Surveys tool (part II) was performed to assess the inherent opinions of forensic odontologists from the the International Organization for Forensic Odonto-Stomatology (IOFOS), and Association of Forensic Odontologists for Human Rights (AFOHR), and Dentify.me. Results were quantitatively evaluated by means of descriptive statistics and qualitatively upon reflection using Microsoft Office Excel (2010). Part I, only one full-text article out of 2235 (Webb et al., 2002) was found eligible indicating a low number of eligible studies. Part II, 75 forensic odontologists and 26 students (49.9% male; 50.5% female) from over 35 countries participated. Results showed that forensic dentists are more psychologically or emotionally affected by child abuse cases and least affected by age estimation cases. Most experienced forensic odontologists reported the lowest scores of discomforts. Males were more comfortable than women in dealing with stress. 80.77% (n= 21) of the students have not experienced any behavioural changes following mortuary sessions but 19.2% (n= 5) witnessed stress. All respondents support the inclusion of a module in Psychology or stress management in training programmes in Forensic Odontology. Suggestions to maintain mental health are considered by the respondents and topics to be taught suggested by a psychologist.

Non-accidental head and neck injuries in children and adolescents.

Child abuse or maltreatment has been a global problem and research shows that more than half of the cases present with head and neck injuries. This study aimed to propose an online referencing platform for dental professionals to know more about signs of child abuse and neglect (CAN) and how suspicious head and neck injuries might look like in real-life scenarios by proposing a 3D design. The study was divided into two parts: i) Integrative literature review, ii) Survey. The first part included an integrative literature review to check if there are enough publications by dentists containing real-life images of injuries related to CAN. Using appropriate keywords and searching across four well-known databases 264 publications relevant to CAN were found, of which, only 3 contained real-life images. Part II of the study included a JISC online survey, consisting of two sections, amongst general dentists, pediatric and forensic dentists. The first section of the survey was about the basic knowledge related to CAN management. A total of 61 dentists from 10 different countries filled the survey, of which 83.1% had seen common head and neck injuries involved in CAN, 61% knew about the dentist's role in reporting such cases, and 66.1% were familiar with local law enforcements to contact. The second section of the survey involved going through real-life scenarios to check the participants understanding of how to tackle a real-life case concluding that only 4-10 participants managed to figure the aspects vital to check before reporting such cases which include a proper detailed history, any previous injuries and their stage of progression, clinical examination of injuries and finally whether the injuries are consistent with the history given. To conclude, there is insufficient representation of the real-life head and neck injuries for dentists to see related to child abuse. Also, all specialists agreed that they require further training regarding CAN management with real-life examples. To address this, a 3d model of commonly seen head and neck injuries in CAN along with some other tools, was created for training and educational purposes and was embedded in a website https://3datlasofchildabuse.webflow.io/.

Pakistan's position in the world of forensic odontology and dental records.

BACKGROUND: Forensic Odontology (FO) still strives for recognition in some countries such as Pakistan. Natural and man-made disasters, along with child abuse cases and age estimation for child marriages and juvenile imprisonments in Pakistan justify its applicability. AIMS: This study investigated the awareness, information, training, practice and interest in FO in dental professionals in Pakistan. Another aim was to design tools to deliver primary knowledge about FO and emphasize the importance of dental records. METHODOLOGY: A 10 question paper-based survey was distributed among 560 dental professionals and postgraduates of 14 public dental institutes/hospitals in Pakistan. The results were quantitatively analyzed by graphs using Microsoft Excel (version 16.22). An educational video and an information leaflet were produced after the survey was undertaken to explain the scope of FO and the importance of dental records respectively. RESULTS: 476 dentists (51%♀, 49 %♂) aged 20 - 50+ years responded and 98.53% confirmed that FO was not taught in the dental schools. 66% were aware of the field and 62% were only informed. 99% were not trained and 89.7% were not working in this field; however, 89% were interested in training within Pakistan. Considering dental charts, 60.92% do not produce detailed charts but 55% maintain them and the majority do so manually. Radiographs were the most stored type. CONCLUSIONS: Most dentists are aware of the existence of FO, but they need to acknowledge the significance of dental record keeping and encourage implementation of FO. Regardless of the absence of any governing body for FO and negligible education, training and implementation in Pakistan, this field is gradually progressing. The authorities should introduce detailed guidelines for recording, managing and storing dental records. They should ensure the future acknowledgement of this subject in the education system and assign forensic odontologists to the Disaster Victim Identification (DVI) team.