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By quantitatively assessing the exposure of female sex workers to violence in Piauí, Brazil, this study aimed to identify associated risk factors and contribute evidence-based public health strategies and human rights policies, advancing health and gender equality. This was a quantitative, observational, analytical, cross-sectional study. A structured questionnaire was administered to 218 female sex workers, aged 19 to 57 years (mean age = 31.76 ± 8.33 years), who were actively working in the state of Piauí, Brazil. The questionnaire addressed demographic characteristics, work-related practices, and exposure to psychological, physical, and sexual violence. Odds ratios (ORs) for the association between variables and the exposure to violence were calculated. Most participants had incomplete higher education (54.2%), self-identified as Black (42.7%), were single (77.4%), heterosexual (89.8%), and mothers (71.2%). Approximately 50% of participants reported being exposed to some form of violence, with physical violence being the most prevalent (75.7%), followed by psychological (43.0%) and sexual violence (12.2%). The odds of exposure to violence were inversely associated with educational attainment ( p  = 0.026) and were significantly related to marital status ( p  = 0.031), having children ( p  < 0.001), and being pregnant ( p  = 0.001). These findings demonstrate the vulnerability of female sex workers to violence in the State of Piauí, Brazil, and the need for tailored public policies that could also benefit society as a whole, such as promotion to scholar education and sexual health awareness.

Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependent steps. Forensic tools for sexual dimorphism based on morphological dental traits are available but have limited performance. This study aimed to test the application of a machine learning setup to distinguish females and males using dentomaxillofacial features from a radiographic dataset. The sample consisted of panoramic radiographs ( n  = 4003) of individuals in the age interval of 6 and 22.9 years. Image annotation was performed with V7 software (V7labs, London, UK). From Scratch (FS) and Transfer Learning (TL) CNN architectures were compared, and diagnostic accuracy tests were used. TL (82%) performed better than FS (71%). The correct classifications of females and males aged ≥ 15 years were 87% and 84%, respectively. For females and males < 15 years, the correct classifications were 80% and 83%, respectively. The Area Under the Curve (AUC) from Receiver-operating Characteristic (ROC) curves showed high classification accuracy between 0.87 and 0.91. The radio-diagnostic use of CNN for sexual dimorphism showed positive outcomes and promising forensic applications to the field of dental human identification.

This study aimed to assess the prevalence of lip print patterns among males and females, and to test the diagnostic accuracy of lip pattern analysis for sexual dimorphism in forensic dentistry. A systematic literature review was performed following the PRISMA guidelines. The search was performed in six primary databases and three databases to cover part of the grey literature. Observational and diagnostic accuracy studies that investigated lip print patterns through cheiloscopy for sexual dimorphism were selected. Risk of bias was assessed with the Joanna Briggs Institute (JBI) tool. Proportion meta-analysis using random effects was fitted to pool the accuracy of cheiloscopy. The odds of correctly identifying males and females was assessed through a random effects meta-analysis. GRADE approach was used to assess certainty of evidence. The search found 3,977 records, published between 1982 and 2019. Seventy-two studies fulfilled the eligibility criteria and were included in the qualitative analysis (n = 22,965 participants), and twenty-two studies were sampled for meta-analysis. Fifty studies had low risk of bias. Suzuki and Tsuchihashi’s technique was the most prevalent among studies. The accuracy of sexual dimorphism through cheiloscopy ranged between 52.7 and 93.5%, while the pooled accuracy was 76.8% (95% CI = 65.8; 87.7). There was no difference between the accuracy to identify males or females (OR = 0.71; 95% CI = 0.26; 1.99). The large spectrum of studies on sexual dimorphism via cheiloscopy depicted accuracy percentage rates that rise uncertainty and concern. The unclear performance of the technique could lead to wrong forensic practice.

Workplace violence is a significant public health issue, particularly affecting female healthcare professionals. This cross-sectional study aimed to assess the vulnerability of female dentists in São Paulo, Brazil, to workplace violence. A sample of 165 female dentists participated by responding to a structured digital questionnaire. The findings revealed that approximately 55% of the respondents had experienced some form of violence, with verbal and psychological abuse being the most common. Intimidation and stalking by patients or their companions were significant predictors of such incidents, increasing the likelihood of exposure by 16-fold ( p  < 0.001) and 3-fold ( p  = 0.001), respectively. Dentists in public healthcare services and those assisted by clinical staff were more frequently exposed to workplace violence. Among the respondents, 28% sought help, with psychological treatment being the most utilized form of support ( p  < 0.001), especially among less experienced professionals ( p  = 0.034). Although certain environmental and professional variables, such as working alone or during night shifts, did not reach statistical significance, they remained important factors for risk assessment. The results highlight the intimate nature of dental practice as a potential vulnerability factor, particularly due to the prolonged close contact with patients and their companions. This study underscores the importance of implementing preventive and protective strategies, including digital monitoring tools and institutional support, and advocates for accessible mental health resources to help mitigate the impact of workplace violence on female dentists.

Deep learning tools based on computer vision have emerged as alternative methods for assessing radiographic image patterns. These approaches have been explored for various forensic applications, including sex and age estimation. This study aimed to evaluate the diagnostic accuracy of a Convolutional Neural Network (CNN) in classifying radiographic images by sex and age, focusing on the nasal aperture as the morphological feature of interest. The sample comprised 9,349 radiographs annotated for the nasal aperture region. A CNN architecture based on the You Only Look Once series—specifically the intermediate version 11 for object classification (YOLO11m-cls)—was implemented, with training performed using 5-fold cross-validation. The overall accuracy rate was 74% (ranging from 61% to 88%), and the area under the Receiver Operating Characteristic (ROC) curve was 0.74. Correct classification rates were 73% for males and 75.17% for females. Accuracy varied with age, showing a 10% decrease among younger individuals compared to older ones. The study confirmed the reduced expression of sexually dimorphic traits in younger individuals and supported existing recommendations against performing sex estimation in subadults. Within the present methodological framework, the nasal aperture demonstrated limited applicability for sex estimation in the studied sample, with an accuracy rate corresponding to approximately one misclassification out of every four predictions.

Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11,640 panoramic radiographs (9680 used for training and 1960 used for validation) of males (n = 5400) and females (n = 6240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the mandibular left third molar (T38) outlined with a semi-automated contour. DenseNet121 was the Convolutional Neural Network (CNN) of choice and was used with Transfer Learning. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.

Background Dental age estimation (DAE) research has grown rapidly and became one of the biggest topics in forensic odontology. This study aimed to evaluate the DAE research trends over the span of 60 years using bibliometric analysis. Methods Sampling was performed in the Scopus database using a search string (“Dental Age Estimation” OR “Age Determination by Teeth”) to detect DAE-related studies. The search was performed from inception to the year 2022. A data-cleaning intervention using a fuzzy-matching technique was done to unify the author and affiliation name variations. Results The initial search returned 1638 articles, years of publication ranging from 1964 to 2022, with an approximate growth rate of 5.9% a year. Source analysis showed that most of the top article sources were Forensic Science International ( n  = 200). Cameriere R presents the overall highest score (77 articles, Local h-index 30). Authors from Shanghai Jiao Tong University produced the highest number of publications ( n  = 111). The most locally cited study was “ A New System of Dental Age Assessment ” by Demirjian et al. (Hum Biol 45:211-227, 1973) ( n  = 1507). The trending topics analysis shows that earlier DAE studies were focused on dental regressive changes and later changed focus to utilizing technological advancements. Institutions and Author's collaborations were also found to be internationally diverse with 20.82% of the articles being a product of international co-authorships. Conclusions DAE research has grown rapidly helped by multiple advancements in various technological ends. Along with the high demand for DAE analysis, authors and publishers need to continually improve their standards for their respective research and reporting and continue to increase collaboration.

The use of MRI in forensic age estimation has been explored extensively during the last decade. The authors of this paper synthesized the available MRI data for forensic age estimation in living children and young adults to provide a comprehensive overview that can guide age estimation practice and future research. To do so, the authors searched MEDLINE, Embase and Web of Science, along with cited and citing articles and study registers. Two authors independently selected articles, conducted data extraction, and assessed risk of bias. They considered study populations including living subjects up to 30 years old. Fifty-five studies were included in qualitative analysis and 33 in quantitative analysis. Most studies had biases including use of relatively small European (Caucasian) populations, varying MR approaches and varying staging techniques. Therefore, it was not appropriate to pool the age distribution data. The authors found that reproducibility of staging was remarkably lower in clavicles than in any other anatomical structure. Age estimation performance was in line with the gold standard, radiography, with mean absolute errors ranging from 0.85 years to 2.0 years. The proportion of correctly classified minors ranged from 65% to 91%. Multifactorial age estimation performed better than that based on a single anatomical site. The authors found that more multifactorial age estimation studies are necessary, together with studies testing whether the MRI data can safely be pooled. The current review results can guide future studies, help medical professionals to decide on the preferred approach for specific cases, and help judicial professionals to interpret the evidential value of age estimation results.

This study aimed at performing and comparing third molar development staging in extracted teeth (EX), panoramic radiography (PAN), and cone beam computed tomography (CBCT). Extracted third molars (n = 158, 95 maxillary, 63 mandibular) from 102 patients (36 males, 66 females) having at least one preoperative PAN and one CBCT were studied. Third molar development staging was performed in PAN, EX, and CBCT using Gleiser et al. (1955) technique modified by Köhler et al (1994). A polytomous logistic regression model was used to compare the staging performed in EX and CBCT with the gold standard staging in PAN. The pair-wise stage comparisons between third molar modalities revealed 63.3% equal staging. In all other comparisons, a maximum difference of one stage was detected. No statistically significant differences between the three staging modalities were detected (p = 0.26). The comparison between EX and PAN staging revealed higher similarity (p = 0.98 in stages 5–10) than the comparison between CBCT and PAN staging (p = 0.81 in stages 5, 7, and 9, and p = 0.80 in stages 6, 8, and 10). The studied third molar staging technique originally designed in PAN can be applied for third molar staging EX and in CBCT.

Forensic dentistry is still an emerging field in Pakistan. The lack of scientific literature on the topic may lead to difficulties in situations where age estimation has a significant part such as in criminal and civil litigation. In mass disasters such as earthquakes and accidents, the correct investigation of the chronological age can be less troublesome if population-specific evidence is available. This is the rationale that justifies dedicated dental age estimation studies. This cross-sectional study aimed to test the time efficiency, validity and applicability of four dental age estimation methods: two invasive (Bang and Ramm and Lamendin) and two non-invasive (Kvaal and Cameriere) in an adult Pakistani population. A total of 37 teeth collected from a dental hospital in Islamabad, Pakistan, were used. Teeth included the central and lateral incisors, canines, and first and second premolars of males and females. Results were calculated using a Microsoft Office 2007 excel spreadsheet. Overall, Kvaal’s method mean absolute error between chronological and estimated ages (MAE: 12.33) showed the highest variation and Bang and Ramm showed more accurate results in comparison with other methods (MAE: 4.80). It was both time-efficient and practical to use. It can be stated that these were preliminary cross-sectional outcomes and that studies with larger samples are necessary.