Sex estimation: Anatomical references on panoramic radiographs using Machine Learning

Ortiz, AG; Costa, Cláudio; Silva, Ricardo Henrique Alves da; Biazevic, M.G.H.; Michel‐Crosato, Edgard

doi:10.1016/j.fri.2020.200356

Cited by 19 publications

(21 citation statements)

References 19 publications

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“…In terms of the currently reported use of ML in forensic post-mortem imaging, it is in its infancy. ML has only been trialed in a few specific forensic applications including automatic forensic dental identification [ 36 ]; sex determination [ [37] , [38] , [39] ]; the automation of bone age assessment [ 40 , 41 ]; prediction of bone fractures [ 42 ]; and the automatic detection of hemorrhagic pericardial effusion [ 43 ]. As far as we are aware, none of these studies has translated into daily forensic practice, despite the potential to streamline case-work.…”

Section: Forensic Applicationsmentioning

confidence: 99%

“…Limitations included the use of volumetric brain data only, without accounting for other moderating variables and quantitative methods, such as age, functional activity, and structural and functional connectivity. Ortiz et al [ 39 ] compared five different ML techniques in the assessment of panoramic radiographs. The ANN outperformed the rest of the models, including k -NNs and logistic regression, with an accuracy of 89 %.…”

Section: Forensic Applicationsmentioning

confidence: 99%

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Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

Peña-Solórzano

Albrecht

Bassed

et al. 2020

Forensic Science International

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Highlights Machine learning techniques are currently not widely used in addressing forensic imaging problems. Machine learning applications can be transferred from clinical to forensic settings. Convolutional neural networks are outperforming other machine learning techniques in image processing tasks. There is a dearth of quantitative metrics presented for inter-study comparisons. A wider use of histopathological gold standard metrics for assessing algorithm performance is required.

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Section: Forensic Applicationsmentioning

confidence: 99%

Section: Forensic Applicationsmentioning

confidence: 99%

Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

Peña-Solórzano

Albrecht

Bassed

et al. 2020

Forensic Science International

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“…Forensic dentistry employs a wide range of analytical tools for gender determination [ 11 , 12 , 13 , 14 , 15 ]. The traditional methods are based on quantitative or qualitative evaluations of craniofacial and dental morphological features [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Possibility of Human Gender Recognition Using Raman Spectra of Teeth

et al. 2021

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Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

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“…In addition, investigators have developed and applied several statistical techniques for estimating human biological information. Most previous researchers used regression analysis based on principles of linearity in body parts [10][11][12][13], but recently, efforts have been made to improve the accuracy through nonlinear analysis methods such as artificial neural networks [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Missing Value Imputation in Stature Estimation by Learning Algorithms Using Anthropometric Data: A Comparative Study

Son

Kim

2020

Applied Sciences

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Estimating stature is essential in the process of personal identification. Because it is difficult to find human remains intact at crime scenes and disaster sites, for instance, methods are needed for estimating stature based on different body parts. For instance, the upper and lower limbs may vary depending on ancestry and sex, and it is of great importance to design adequate methodology for incorporating these in estimating stature. In addition, it is necessary to use machine learning rather than simple linear regression to improve the accuracy of stature estimation. In this study, the accuracy of statures estimated based on anthropometric data was compared using three imputation methods. In addition, by comparing the accuracy among linear and nonlinear classification methods, the best method was derived for estimating stature based on anthropometric data. For both sexes, multiple imputation was superior when the missing data ratio was low, and mean imputation performed well when the ratio was high. The support vector machine recorded the highest accuracy in all ratios of missing data. The findings of this study showed appropriate imputation methods for estimating stature with missing anthropometric data. In particular, the machine learning algorithms can be effectively used for estimating stature in humans.

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Sex estimation: Anatomical references on panoramic radiographs using Machine Learning

Cited by 19 publications

References 19 publications

Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Missing Value Imputation in Stature Estimation by Learning Algorithms Using Anthropometric Data: A Comparative Study

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