Sex classification of first molar teeth in cone beam computed tomography images using data mining

Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review

BACKGROUND

Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities.

STUDY DESIGN

A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another.

RESULTS

The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection.

CONCLUSION

Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.

Sex determination through maxillary dental arch and skeletal base measurements using machine learning

BACKGROUND

Cranial, facial, nasal, and maxillary widths have been shown to be significantly affected by the individual's sex. The present study aims to use measurements of dental arch and maxillary skeletal base to determine sex, employing supervised machine learning.

MATERIALS AND METHODS

Maxillary and mandibular tomographic examinations from 100 patients were analyzed to investigate the inter-premolar width, inter-molar width, maxillary width, inter-pterygoid width, nasal cavity width, nostril width, and maxillary length, obtained through Cone Beam Computed Tomography scans. The following machine learning algorithms were used to build the predictive models: Logistic Regression, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron Classifier (MLP), Decision Tree, and Random Forest Classifier. A 10-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and Receiver Operating Characteristic (ROC) curves were constructed.

RESULTS

Univariate analysis showed statistical significance (p < 0.10) for all skeletal and dental variables. Nostril width showed greater importance in two models, while Inter-molar width stood out among dental measurements. The models achieved accuracy values ranging from 0.75 to 0.85 on the test data. Logistic Regression, Random Forest, Decision Tree, and SVM models had the highest AUC values, with SVM showing the smallest disparity between cross-validation and test data for accuracy metrics.

CONCLUSION

Transverse dental arch and maxillary skeletal base measurements exhibited strong predictive capability, achieving high accuracy with machine learning methods. Among the evaluated models, the SVM algorithm exhibited the best performance. This indicates potential usefulness in forensic sex determination.

How teeth can be used to estimate sexual dimorphism? A scoping review

INTRODUCTION

Teeth are biological structures with a high degree of hardness, density, calcification, and capacity to adapt to extrinsic factors at physical, biological, and physiological levels. Subsequently, they resist for a longer period in deteriorating environmental conditions. With dental analysis, it is possible to acquire biographical data about a person. The aim of this scoping review was to identify publications using human teeth tissues to estimate sexual dimorphism.

METHODS

The scoping review was carried out in the following databases: Jstor, Scielo, Science Direct, PubMed, and Scopus, using ten search strategies in English and guaranteeing completeness and reproducibility of the phases stipulated in the PRISMA guide.

RESULTS

143 studies on sexual dimorphism based on dental tissue traits were included, of which 40.6% (n = 58) were done in Asia and 27.2% (n = 39) in America. 80% of the studies (equivalent to 114 articles) focused their observations and measurements on the dental crown; 4.2% in enamel, dentin, and pulp together; 3.5% in dental pulp; 2.1% in the entire tooth; 2.8% in enamel, root, and the enamel-cementum junction, and only 0.7% in dentin and pulp. In addition, 92.3% of the studies used metric methods, while only 4.9% and 2.8% used biochemical and non-metric method respectively.

CONCLUSION

For sexual dimorphism establishment, enamel has been the most analyzed dental tissue in permanent canines and molars mainly. Likewise, the most widely and accurately used methods for this purpose are the metrics, with the odontometry as the most implemented (intraoral or by using dental plaster models, digital scanning or software) with prediction percentages ranging from 51% to 95.9%. In contrast to biochemical methods, that can achieve the highest precision (up to 100%), the non-metric methods, to a less extent, reported prediction percentages of 58%.

Sexual dimorphism in odontometric parameters using cone beam CT: a systematic review

OBJECTIVE

To determine whether odontometric parameters using cone beam computed tomography (CBCT) would aid in sex estimation by assessing sexual dimorphism of odontometric parameters.

MATERIAL AND METHODS

The focused question was whether there is sexual dimorphism in linear and volumetric odontometric parameters when assessed using CBCT. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines were followed to conduct a systematic search until June 2022 in all major databases. Data were extracted regarding the population, size of the sample, age range, teeth analyzed, linear or volumetric measurements, accuracy, and conclusion. The quality of included studies was assessed using (Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.

RESULTS

Out of the 3761 studies identified, twenty-nine full-text articles were assessed for eligibility. Finally, twenty-three articles (4215 participants) that provided data on odontometrics using CBCT were included in this systematic review. The odontological sex estimation were assessed either linear measurements (n = 13) or volumetric measurements (n = 8) or both (n = 2). Canines were analysed in maximum number of reports (n = 14), followed by incisors (n = 11), molars(n = 10) and premolars(n = 6). Most of the reports (n = 18) confirmed the existence of sexual dimorphism in odontometric parameters when assessed using CBCT. No significant differences in odontometrics between the sexes were noted in some reports (n = 5). The accuracy of sex estimation was assessed in eight investigations, which ranged from 47.8 to 92.3%.

CONCLUSIONS

Odontometrics of human permanent dentition using CBCT exhibit a certain degree of sexual dimorphism. Both linear and volumetric measurements of teeth can aid sex estimation.

Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives

In recent years, new studies based on artificial intelligence (AI) have been conducted in the forensic field, posing new challenges and demonstrating the advantages and disadvantages of using AI methodologies to solve forensic well-known problems. Specifically, AI technology has tried to overcome the human subjective bias limitations of the traditional approach of the forensic sciences, which include sex prediction and age estimation from morphometric measurements in forensic anthropology or evaluating the third molar stage of development in forensic odontology. Likewise, AI has been studied as an assisting tool in forensic pathology for a quick and easy identification of the taxonomy of diatoms. The present systematic review follows the PRISMA 2020 statements and aims to explore an emerging topic that has been poorly analyzed in the forensic literature. Benefits, limitations, and forensic implications concerning AI are therefore highlighted, by providing an extensive critical review of its current applications on forensic sciences as well as its future directions. Results are divided into 5 subsections which included forensic anthropology, forensic odontology, forensic pathology, forensic genetics, and other forensic branches. The discussion offers a useful instrument to investigate the potential benefits of AI in the forensic fields as well as to point out the existing open questions and issues concerning its application on real-life scenarios. Procedural notes and technical aspects are also provided to the readers.

Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review

Background

Forensic odontology may require a visual or clinical method during identification. Sometimes it may require forensic experts to refer to the existing technique to identify individuals, for example, by using the atlas to estimate the dental age. However, the existing technology can be a complicated procedure for a large-scale incident requiring a more significant number of forensic identifications, particularly during mass disasters. This has driven many experts to perform automation in their current practice to improve efficiency.

Objective

This article aims to evaluate current artificial intelligence applications and discuss their performance concerning the algorithm architecture used in forensic odontology.

Methods

This study summarizes the findings of 28 research papers published between 2010 and June 2022 using the Arksey and O'Malley framework, updated by the Joanna Briggs Institute Framework for Scoping Reviews methodology, highlighting the research trend of artificial intelligence technology in forensic odontology. In addition, a literature search was conducted on Web of Science (WoS), Scopus, Google Scholar, and PubMed, and the results were evaluated based on their content and significance.

Results

The potential application of artificial intelligence technology in forensic odontology can be categorized into four: (1) human bite marks, (2) sex determination, (3) age estimation, and (4) dental comparison. This powerful tool can solve humanity's problems by giving an adequate number of datasets, the appropriate implementation of algorithm architecture, and the proper assignment of hyperparameters that enable the model to perform the prediction at a very high level of performance.

Conclusion

The reviewed articles demonstrate that machine learning techniques are reliable for studies involving continuous features such as morphometric parameters. However, machine learning models do not strictly require large training datasets to produce promising results. In contrast, deep learning enables the processing of unstructured data, such as medical images, which require large volumes of data. Occasionally, transfer learning was used to overcome the limitation of data. In the meantime, this method's capacity to automatically learn task-specific feature representations has made it a significant success in forensic odontology.

Cranial and Odontological Methods for Sex Estimation—A Scoping Review

The estimation of sex from osteological and dental records has long been an interdisciplinary field of dentistry, forensic medicine and anthropology alike, as it concerns all the above mentioned specialties. The aim of this article is to review the current literature regarding methods used for sex estimation based on the skull and the teeth, covering articles published between January 2015 and July 2022. New methods and new approaches to old methods are constantly emerging in this field, therefore resulting in the need to summarize the large amount of data available. Morphometric, morphologic and biochemical analysis were reviewed in living populations, autopsy cases and archaeological records. The cranial and odontological sex estimation methods are highly population-specific and there is a great need for these methods to be applied to and verified on more populations. Except for DNA analysis, which has a prediction accuracy of 100%, there is no other single method that can achieve such accuracy in predicting sex from cranial or odontological records.

Sex estimation using foramen magnum measurements, discriminant analyses and artificial neural networks on an eastern Turkish population sample

BACKGROUND

Although many studies have been conducted using the foramen magnum for sex estimation, recent findings have indicated that the discriminant and regression models obtained from the foramen magnum may not be reliable. Artificial Neural Networks, was used as a classification technique in sex estimation studies on some other bones, did not used in sex estimation studies on the foramen magnum until now. The aim of this study was sex estimation on an Eastern Turkish population sample using foramen magnum measurements, discriminant analyses and Artificial Neural Networks.

METHODOLOGY

The study was performed on the CT images of a total of 720 cases, comprising 360 males and 360 females. For sex estimation, discriminant analysis and Artificial Neural Networks were used.

RESULTS

The accuracy rate was 86.7% with discriminant analysis and when sex estimation accuracy was determined according to cases with posterior probabilities above 95%, the accuracy ranged from 0% to 33.3%. With the use of the discriminant formulas of 2 other studies, obtained from different Turkish samples, sex could be determined at a rate of 84.6%. Some formulas were found to be unsuccessful in sex estimation. Sex estimation accuracy of 88.2% was achieved with Artificial Neural Networks.

CONCLUSION

In this study, it was found that sex could be determined to some extent with discriminant formulas from other samples from the same population, although some formulas were unsuccessful. With the use of image processing techniques and machine learning algorithms, better results can be obtained in sex estimation.