Performance of dental pattern analysis system with treatment chronology on panoramic radiography

Can deep learning identify humans by automatically constructing a database with dental panoramic radiographs?

The aim of this study was to propose a novel method to identify individuals by recognizing dentition change, along with human identification process using deep learning. Recent and past images of adults aged 20-49 years with more than two dental panoramic radiographs (DPRs) were assumed as postmortem (PM) and antemortem (AM) images, respectively. The dataset contained 1,029 paired PM-AM DPRs from 2000 to 2020. After constructing a database of AM dentition, the degree of similarity was calculated and sorted in descending order. The matched rank of AM identical to an unknown PM was measured by extracting candidate groups (CGs). The percentage of rank was calculated as the success rate, and similarity scores were compared based on imaging time intervals. The matched AM images were ranked in the CG with success rates of 83.2%, 72.1%, and 59.4% in the imaging time interval for extracting the top 20.0%, 10.0%, and 5.0%, respectively. The success rates depended on sex, and were higher for women than for men: the success rates for the extraction of the top 20.0%, 10.0%, and 5.0% were 97.2%, 81.1%, and 66.5%, respectively, for women and 71.3%, 64.0%, and 52.0%, respectively, for men. The similarity score differed significantly between groups based on the imaging time interval of 17.7 years. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in effectively reducing the size of AM CG in identifying humans.

The radiographic diversity of dental patterns among 7219 young individuals-a contribution to disaster victim identification

The diversity of dental patterns is a fundamental topic in disaster victim identification. The current scientific literature, however, is scarce of data regarding young individuals. This study aimed to assess the radiographic diversity of dental patterns, considering missing, unrestored, and filled teeth in young individuals. The sample consisted of 7219 panoramic radiographs of individuals between 12 and 22.9 years. The permanent teeth, except third molars, were coded as missing, unrestored, or filled and odds ratios (OR) were calculated based on sex, dental arch, and age. The sex-combined sample had 1.116 distinctive dental patterns. "All unrestored" teeth was the most common pattern (OR: 0.437) followed by the sequence of unrestored teeth except restored mandibular first molars (OR: 0.021). Females had more distinctive dental patterns than males (p < .001), while males had more unrestored teeth (p < .001). In the age category of 12-12.9 years, the OR for finding a distinctive dental pattern was 11%, while in the age category of 22-22.9 years it increased to 58%. On the other hand, the OR for "all unrestored" gradually decreased according to age (74% in the younger category, and 23% in the older age category). The distinctiveness of dental patterns among young individuals is affected by the predominance of unrestored teeth. However, registering a single filled tooth in a remaining unrestored dentition can reduce exponentially the probability of finding an identical pattern of missing, unrestored and filled teeth.

The radiographic diversity of dental patterns for human identification - Systematic review and meta-analysis

This study aimed to revisit the scientific literature related to the diversity of dental patterns observed in radiographs. The rationale was to find evidence to support dental human identifications. A systematic review was performed following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Strategic search was accomplished in five electronic data sources (SciELO, Medline/Pubmed, Scopus, Open Grey and OATD) were searched. The study model of choice was observational analytical cross-sectional. The search resulted 4.337 entries. The sequential screening based on title, abstract and full-text reading led to 9 eligible studies (n = 5.700 panoramic radiographs) published between 2004 and 2021. Studies from Asian countries were predominant (e.g., South Korea, China, and India). All the studies showed low risk of bias (measured according to the Johanna Briggs Institute's critical appraisal tool for observational cross-sectional studies). Morphological, therapeutic, and pathological identifiers were charted from radiographs to create dental patterns across studies. Six studies (n = 2.553 individuals) had similar methodology and outcome metrics and were included in the quantitative analysis. A meta-analysis was performed and revealed a pooled diversity of the human dental pattern of 0.979 combining maxillary and mandibular teeth. The additional subgroup analysis with maxillary and mandibular teeth have a diversity rate of 0.897 and 0.924, respectively. The existing literature shows that human dental patterns are highly distinctive, especially if morphological, therapeutic and pathological dental features are combined. The diversity of dental identifiers found in the maxillary, mandibular and combined arches is hereby corroborated by this meta-analyzed systematic review. These outcomes support applications for evidence-based human identification.

Dental pattern diversity in a military population and its usefulness for assessing the degree of certainty in dental identification

In forensic dentistry, the analysis of dental diversity forms the basis of probability calculations in dental identification. The present study aimed to contribute to the knowledge of dental diversity in a Spanish military population (considering isolated teeth, sets of different numbers of teeth, and combinations of teeth of forensic interest) and its implications for dental identification. A further aim was to compare the performance of three coding systems (detailed, generic, and binary) to assess dental pattern diversity. Dental diversity of a representative sample of the Spanish military population (3920 individuals aged between 18 and 55 years) was calculated according to a genetic (mitochondrial DNA) model in which diversity was defined as the likelihood that two randomly selected individuals in a sample would exhibit different patterns. By performing all pairwise comparisons of dental patterns in the dataset, the total number of matches was generated, and the diversity of dental patterns was then derived. First and third molars were the teeth that showed the highest levels of diversity, and a high diversity value (>0.99) was obtained with only 5 teeth (16, 36, 38, 46, and 48) when detailed coding was used. In addition, dental diversity in the full dentition and posterior teeth exceeded the threshold of 0.99 in all three coding systems. Although a very high diversity value (≥0.999) was only achieved with detailed coding, it should be noted that the generic coding system requires less time and skill to use, and can also provide high diversity values. Our findings show that further efforts should be made to establish large, periodically updated dental datasets of different populations in order to assess dental pattern diversity (without excluding third molars) based on empirical comparison, and to substantiate the certainty of dental identification.

Human Remains Identification Using Micro-CT, Chemometric and AI Methods in Forensic Experimental Reconstruction of Dental Patterns after Concentrated Sulphuric Acid Significant Impact

(1) Teeth, in humans, represent the most resilient tissues. However, exposure to concentrated acids might lead to their dissolving, thus making human identification difficult. Teeth often contain dental restorations from materials that are even more resilient to acid impact. This paper aims to introduce a novel method for the 3D reconstruction of dental patterns as a crucial step for the digital identification of dental records. (2) With a combination of modern methods, including micro-computed tomography, cone-beam computer tomography, and attenuated total reflection, in conjunction with Fourier transform infrared spectroscopy and artificial intelligence convolutional neural network algorithms, this paper presents a method for 3D-dental-pattern reconstruction, and human remains identification. Our research studies the morphology of teeth, bone, and dental materials (amalgam, composite, glass-ionomer cement) under different periods of exposure to 75% sulfuric acid. (3) Our results reveal a significant volume loss in bone, enamel, dentine, as well as glass-ionomer cement. The results also reveal a significant resistance by the composite and amalgam dental materials to the impact of sulfuric acid, thus serving as strong parts in the dental-pattern mosaic. This paper also probably introduces the first successful artificial intelligence application in automated-forensic-CBCT segmentation. (4) Interdisciplinary cooperation, utilizing the mentioned technologies, can solve the problem of human remains identification with a 3D reconstruction of dental patterns and their 2D projections over existing ante-mortem records.

Dental Radiographic/Digital Radiography Technology along with Biological Agents in Human Identification

The heavy casualties associated with mass disasters necessitate substantial resources to be managed. The unexpectedly violent nature of such occurrences usually remains a problematic amount of victims that urgently require to be identified by a reliable and economical method. Conventional identification methods are inefficient in many cases such as plane crashes and fire accidents that have damaged the macrobiometric features such as fingerprints or faces. An appropriate recognition method for such cases should use features more resistant to destruction. Forensic dentistry provides the most appropriate available method for the successful identification of victims using careful techniques and precise data interpretation. Since bones and teeth are the most persistent parts of the demolished bodies in sudden mass disasters, scanning and radiographs are unrepeatable parts of forensic dentistry. Forensic dentistry as a scientific method of human remain identification has been considerably referred to be efficient in disasters. Forensic dentistry can be used for either "sex and age estimation," "Medical biotechnology techniques," or "identification with dental records," etc. The present review is aimed at discussing the development and implementation of forensic dentistry methods for human identification. For this object, the literature from the last decade has been searched for the innovations in forensic dentistry for human identification based on the PubMed database.

A pilot study of an automated personal identification process: Applying machine learning to panoramic radiographs

Purpose

This study aimed to assess the usefulness of machine learning and automation techniques to match pairs of panoramic radiographs for personal identification.

Materials and Methods

Two hundred panoramic radiographs from 100 patients (50 males and 50 females) were randomly selected from a private radiological service database. Initially, 14 linear and angular measurements of the radiographs were made by an expert. Eight ratio indices derived from the original measurements were applied to a statistical algorithm to match radiographs from the same patients, simulating a semi-automated personal identification process. Subsequently, measurements were automatically generated using a deep neural network for image recognition, simulating a fully automated personal identification process.

Results

Approximately 85% of the radiographs were correctly matched by the automated personal identification process. In a limited number of cases, the image recognition algorithm identified 2 potential matches for the same individual. No statistically significant differences were found between measurements performed by the expert on panoramic radiographs from the same patients.

Conclusion

Personal identification might be performed with the aid of image recognition algorithms and machine learning techniques. This approach will likely facilitate the complex task of personal identification by performing an initial screening of radiographs and matching ante-mortem and post-mortem images from the same individuals.

Specific oral and maxillofacial identifiers in panoramic radiographs used for human identification

Radiographically assisted dental identification is an important means for individual identification. Specific identifiers help to quickly filter some of the possible corresponding AM and PM images at the beginning. The study seeks specific oral and maxillofacial identifiers in panoramic radiographs. A total of 920 panoramic radiographs from 460 live patients were used. The most recent radiograph served as the surrogate post-mortem (PM) record of an unidentified person, and the earliest radiograph served as the ante-mortem (AM) record of the same person. We evaluated the following four groups of identifiers of the images: (1) dental morphology, tooth number, and position; (2) dental treatment and pathology; (3) morphological identifiers of the jaw; and (4) pathological identifiers of the jaw. The ratio of each identifier being identified simultaneously in the AM and PM databases was determined. Specific identifiers were defined as those that appeared at low frequency (ratio: 0%-0.250%). A total of 18 specific oral and maxillofacial identifiers were determined. The specific identifiers were a retained deciduous tooth (0.011%), S-shaped deflection of a tooth root (0.012%), distal deflection of tooth root (0.017%), inverted impaction (0.018%), malposition (0.038%), supernumerary teeth (0.061%), mesial deflection of tooth root (0.092%), microdontia (0.136%), buccal/lingual impaction (0.188%), cementoma (0.002%), hypercementosis (0.002%), continuous crown (0.004%), pulp calcification (0.023%), attrition (0.030%), residual root (0.106%), root resorption (0.137%), implant (0.156%), and osteomyelitis (0.002%). Identifiers of the teeth and jaw can be used for human identification, and dental identifiers are more specific than identifiers of jaw.