Sex Prediction Based on Mesiodistal Width Data in the Portuguese Population

Neves, João Albernaz; Antunes‐Ferreira, Nathalie; Machado, Vanessa; Botelho, João; Proença, Luís; Quintas, Alexandre; Mendes, José João; Delgado, Ana Sintra

doi:10.3390/app10124156

Cited by 6 publications

(8 citation statements)

References 30 publications

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“…In 2020, Neves et al [ 21 ] developed a predictive model for gender identification based on mesiodistal widths using a permanent dental cast. A total of 168 dental casts were considered for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Santosh¹,

Pradeep²,

Goel³

et al. 2022

Journal of Healthcare Engineering

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The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth. Since teeth are a strong and unique part of the human body that exhibits least subject to risk in natural structure and remains unchanged for a longer duration, the process of identification of gender- and age-related information from human beings is systematically carried out by analyzing OPG (orthopantomogram) images. A total of 1142 digital X-ray images of teeth were obtained from dental colleges from the population of the middle-east part of Karnataka state in India. 80% of the digital images were considered for training purposes, and the remaining 20% of teeth images were for the testing cases. The proposed gender and age determination system finds its application widely in the forensic field to predict results quickly and accurately. The prediction system was carried out using Multiclass SVM (MSVM) classifier algorithm for age estimation and LIBSVM classifier for gender prediction, and 96% of accuracy was achieved from the system.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Santosh¹,

Pradeep²,

Goel³

et al. 2022

Journal of Healthcare Engineering

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show abstract

“…In such circumstances, teeth are considered quite useful as they are often recovered intact. 8 Identification of gender with the help of dental features can be categorized into metric and non metric methods. Non metric features methods are based on presence or absence of particular morphological dental features such as cusp of carabelli, upper incisor shovelling, hypocone and protostylid whereas metric features are based on tooth measurements.…”

Section: Discussionmentioning

confidence: 99%

Comparative assessment of odontometric parameters for gender determination: A hospital based cross-sectional study

Lomi¹,

M²

2021

IJFCM

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Determining gender is one of the most important procedure in forensics. Teeth are made of the most enduring mineralized tissues in the body, as such, they have an extraordinary resistance to putrefaction and the effects of external agents (physical, thermal, mechanical, chemical or biological) which makes them invaluable material for anthropological, genetic, odontologic and forensic examinations.i)To assess the degree of sexual dimorphism in maxillary canine using maxillary canine index, maxillary first molar and central incisor using mesiodistal (MD) and buccolingual/buccopalatal (BL) dimensions of the crown. ii) To determine the accuracy of maxillary canine index, first molar and central incisor dimensions (mesiodistal and buccolingual/buccopalatal) in determining sex.This study consisted of total 30 participants (males=15, females=15) of age group ranging from 20 to 35 yrs. Measurement of dimensions of selected teeth were performed on maxillary cast using digital Vernier caliper, resolution 0.01mm. Data thus obtained were analyzed statistically.The mean values of all the measurements in males were greater than females. There was statistical significant difference in mean value of bucco-lingual width of molar, mesiodistal width of canine (on left side, right side and average) and the inter-canine distance between males and females. The buccolingual width of molar showed the highest % of sexual dimorphism (5.33%) and the mesiodistal width of molar showed the least % of dimorphism (0.7%). Bucco-lingual width of molar posed the highest prediction accuracy for gender determination. We conclude that buccolingual width of maxillary first molar can be used to predict the gender when only teeth of maxillary arch are available for forensic examination.

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“…Specifically for sex estimation based on x-ray images, one study achieved an accuracy between 68% and 80% [41] with a sample size of 200 by using measurements of mandibular teeth, and another study [42] achieved an accuracy of 83.3% with a sample size of 60 by using diagonal measurements. While the systematic review is from 2017, newer studies use a similar approach and achieve similar results [43]. Similarly to age estimation, the parameters of the established models can be adjusted to improve accuracy on different populations [44]- [51].…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Exploration of Neural Networks for Forensic Analysis of Adult Single Tooth X-Ray Images

et al. 2022

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Determining the demographic characteristics of a person post-mortem is a fundamental task for forensic experts, and the dental system is a crucial source of those information. Those characteristics, namely age and sex, can reliably be determined. The mandible and individual teeth survive even the harshest conditions, making them a prime target for forensic analysis. Current methods in forensic odontology rely on time-consuming manual measurements and reference tables, many of which rely on the correct determination of the tooth type. This study thoroughly explores the applicability of deep learning for sex assessment, age estimation, and tooth type determination from x-ray images of individual teeth. A series of models that use state-of-the-art feature extraction architectures and attention have been trained and evaluated. Their hyperparameters have been explored and optimized using a combination of grid and random search, totaling over a thousand experiments and 14076 hours of GPU compute time. Our dataset contains 86495 individual tooth x-ray image samples, with a subset of 7630 images having additional information about tooth alterations. The best-performing models are fine-tuned, the impact of tooth alterations is analyzed, and model performance is compared to current methods in forensic odontology literature. We achieve an accuracy of 76.41% for sex assessment, a median absolute error of 4.94 years for age estimation, and an accuracy of 87.24% to 99.15% for tooth type determination. The constructed models are fully automated and fast, their results are reproducible, and the performance is equal to or better than current state-of-the-art methods in forensic odontology.

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Sex Prediction Based on Mesiodistal Width Data in the Portuguese Population

Cited by 6 publications

References 30 publications

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Comparative assessment of odontometric parameters for gender determination: A hospital based cross-sectional study

A Comprehensive Exploration of Neural Networks for Forensic Analysis of Adult Single Tooth X-Ray Images

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