Forensic odontology identification response to terrorist attacks in Paris November 2015

Insights from the 7th of October massacre: Forensic odontology in mass disasters

On October 7, 2023, an attack on Israel led to 1200 Israeli fatalities, creating a critical need for victim identification under challenging conditions of the remains, which ranged from fresh to severely burnt and fragmented. This study examines the role of forensic odontology within a multidisciplinary approach to disaster victim identification, a key element in handling mass casualty events. Ante-mortem dental data, obtained from various sources, were matched with postmortem information, such as full mouth X-rays and cone-beam computed tomography scans, utilizing dental identification software and visual comparison methods. A total of 970 victims were examined, leading to the successful identification of 166 individuals, representing 17 % of the overall identifications made through all scientific means. These findings emphasize the value of dental interventions and morphological features in forensic identifications, depicted in computed tomography-generated panoramic images which can provide an effective alternative to full mouth X-rays when direct oral access was restricted. These insights contribute to advancing forensic practices in response to complex mass disaster situations.

Positive identification through comparative dental analysis in mass disaster: a systematic review and meta-analysis

PURPOSE

The study aimed to assess the probability of achieving positive identification through comparative dental analysis (CDA) and to determine the factors that influence its success rate in mass disaster scenarios.

METHODS

An electronic literature search was conducted across six databases for observational studies that reported both the total number of mass disaster victims and the count of victims identified through CDA alone. A random-effect meta-analysis, using the proportion of victims identified with CDA as the effect size, was conducted alongside subgroup analyses based on the type of disaster (natural or non-natural), the disaster classification (open or closed), and the geographical region (i.e., Europe, Asia).

RESULTS

The search yielded 3133 entries, out of which 32 studies were deemed eligible. Most of the studies (96.8%) presented a low risk of bias. The meta-analysis revealed a mean weighted-proportion probability of 0.32, indicating that forensic odontology could identify about one-third of the victims in a mass disaster. The probability of comparative dental identification was three times higher in closed mass disasters compared to open disasters (p < 0.05) and was higher in mass disasters occurring in North America and Europe compared to other regions (p < 0.05).

CONCLUSION

The current result suggested that CDA can identify approximately 32% of a victim in a hypothetical scenario, emphasizing the integral role of teeth and forensic odontology in victim identification framework.

[Forensic dentistry for identity verification. A survey at the state police level]

BACKGROUND

In Germany, the identification of unknown dead persons is the responsibility of the police. According to INTERPOL standards, primary (e.g., DNA, fingerprints, and teeth) and secondary (e.g., tattoos) characteristics are examined. Forensic dentistry is already used internationally as an efficient method. In this study, the approach of state police in Germany was analyzed. The methods used for identification, the role of forensic dentistry, the cooperation with dentists, and possible optimization approaches are investigated.

METHODS

By means of a digital questionnaire, police officers competent in all federal states for the discovery of unknown dead bodies were asked about identification methods and specifically about the use of forensic dentistry.

RESULTS

Eighty-five officers from at least 11 federal states participated in the survey. The procedure turned out to be department specific. In 72.6% of the cases, different characteristics are combined in the identification process, most frequently DNA with dental status (37.1%). DNA analysis is used most frequently. Of the respondents, 62.9% agreed that dental identification is used "often." The percentage of identifications using dental status is estimated to be 1.6-8.1%. For forensic dentistry, 19.4% have a fixed point of contact. A digital platform to contact dentists was estimated to be helpful by 56.5%.

DISCUSSION

Forensic dentistry is currently still lagging behind DNA analysis, which could change through increasing digitalization if, for example, ante-mortem data are more reliably available and platforms for interdisciplinary exchange are created.

Identification of victims of the collapse of a mine tailing dam in Brumadinho

The collapse of the B1 Dam of VALE SA mining company in Brumadinho, Minas Gerais, Brazil was the largest humanitarian disaster and occupational accident in the country's history, and it posed challenges regarding the management and identification of multiple victims. We evaluated the impact of the iron ore tailings on the victims' bodies. We examined the scientific identification of the victims and the dynamics of the disaster over the 1st year after it occurred. We also determined the socio-demographic profiles of the victims. In this retrospective, cross-sectional study, we investigated the expert reports of the victims' biological remains from 25 January 2019 to 25 January 2020. We analysed the socio-demographic data, identification methods, identification status, identification time, and necroscopic information. During the study period, 259 of 270 victims were identified, and 603 biological materials were analysed; among them, 86.2% were body parts and 13.8% were whole bodies. Of the total cases registered that year, 476 (78.9%) were submitted during the first 10 weeks after the disaster. Friction ridge analysis accounted for 67.9% of primary identifications and DNA analysis did so for 91.6% of re-identification cases. Body dismemberment was 3.4 times greater among mine workers than among community victims. Adult males accounted for the greatest number of victims (P < 0.001). Polytraumatic injury was the prevalent single cause of death. Necropsy examination revealed the occurrence of asphyxia in 7% of cases. The higher number of fatalities and greater dismemberment among employees than with community residents underlines the occupational dangers in the mining industry and clarifies the dynamics of the disaster. In the initial weeks after the dam collapsed, friction ridge analysis was the most appropriate method for identification. Subsequently, DNA analysis became the most-used technique for identification and re-identification owing to the great volume of body parts and decomposed biological tissue. Autopsy allowed diagnosis of the causes of death to be clarified according to the Brazilian criminal legal system.

Automatic detection of teeth and dental treatment patterns on dental panoramic radiographs using deep neural networks

Disaster victim identification issues are especially critical and urgent after a large-scale disaster. The aim of this study was to suggest an automatic detection of natural teeth and dental treatment patterns based on dental panoramic radiographs (DPRs) using deep learning to promote its applicability as human identifiers. A total of 1 638 DPRs, of which the chronological age ranged from 20 to 49 years old, were collected from January 2000 to November 2020. This dataset consisted of natural teeth, prostheses, teeth with root canal treatment, and implants. The detection of natural teeth and dental treatment patterns including the identification of teeth number was done with a pre-trained object detection network which was a convolutional neural network modified by EfficientDet-D3. The objective metrics for the average precision were 99.1% for natural teeth, 80.6% for prostheses, 81.2% for treated root canals, and 96.8% for implants, respectively. The values for the average recall were 99.6%, 84.3%, 89.2%, and 98.1%, in the same order, respectively. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in automatically identifying teeth number and detecting natural teeth, prostheses, treated root canals, and implants.

It is useful to use dental panoramic radiographs to perform the disaster victim identification (DVI).