Bite marks on skin and clay: A comparative analysis

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.

Superimposed polygonal approximation analysis comparing 2D photography and 3D scanned images of bite marks on human skin

Background

Preservation of bite marks evidence has always been a major problem in forensic odontology due to progressive loss of details as time passes. The use of 2D photographs has been widely used to document forensic evidence and preserving bite marks; however, there are limitations to this method. This study aims to measure the accuracy of the 3D scanned image in comparison to 2D photograph registration of experimental bite marks. Thirty volunteers performed self-exertions of a bite mark on the respective forearm of subjects. A 2D photograph and 3D scanned image was immediately registered following bite mark exercise using a conventional camera and Afinia EinScan-Pro 2X PLUS Handheld 3D Scanner, respectively. The outlines of the bite mark were transformed into a polygonal shape. Next, the polygonal approximation analysis was performed by an arbitrary superimposition method. The difference between surface areas of both images was calculated (2D photographs ̶ 3D scanned images).

Results

A paired t test was used to measure significance with α = 0.05. The mean surface area of 2D photographs and 3D scanned images is 31.535 cm2 and 31.822 cm2, respectively. No statistical difference was found between both mean surface areas (p > 0.05). The mean error (ME) is 0.287 ± 3.424 cm2 and the mean absolute error (MAE) is 1.733 ± 1.149 cm2.

Conclusion

Bite marks registered with the 3D scanned image are comparable to the standard 2D photograph for bite mark evaluations. The use of a 3D scan may be adopted as a standard operating procedure in the forensic application, especially for evidence preservation.

A Novel Computer-Assisted Method of Bite Mark Analysis for Gender Determination

Background

Bite mark analysis is an imperative area of forensic odontology and considered the commonest form of dental evidence presented in the criminal court. The process of comparing bite marks with a suspect's dentition includes analysis and measurement of shape, size, and position of an individual's teeth. The present study was aimed to evaluate the bite marks of males and females using a novel indirect computer-assisted method and explicate its application in forensic odontology.

Materials and methods

60 subjects (30 males and 30 females) with normal occlusion were included in the present study. Bite registrations were obtained with help of modelling waxes, and positive replicas were prepared with dental stone and barium powder. Intraoral periapical radiographs were taken for the same. The radiographs obtained were scanned and analyzed by measuring tools using Sidexis Next Generation software. Intercanine distance (ICD), line AB, angle ABX, and angle ABY were measured. The Kruskal–Wallis test was performed to compare the bite marks of males and females.

Results

The mean ICD of males and females was found to be 32.95 mm and 29.84 mm, respectively, and was statistically highly significant with a p value <0.001. The mean ICD, line AB, and angle ABX were found to be higher in males when compared to females.

Conclusion

Analysis of bite marks using this novel computer-assisted method is a simple, reliable, easily reproducible, and economical technique with confidentiality of the identity of the participants involved.