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

Santosh, K. C.; Pradeep, N.; Goel, Vinod; Ranjan, Raju; Pandey, Ekta; Shukla, Piyush Kumar; Nuagah, Stephen Jeswinde

doi:10.1155/2022/8302674

Cited by 22 publications

(23 citation statements)

References 26 publications

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“…Previous studies have shown that artificial intelligence or machine learning algorithms can improve sex determination accuracy and lead to better results 4 . These methods have become increasingly popular in recent years because of their classification capacity and lack of strict criteria and assumptions, such as discriminant function analysis or regression analysis 48–52 . In the current study, machine learning algorithms were used to determine whether they could provide an improvement.…”

Section: Discussionmentioning

confidence: 99%

Sex Estimation From Measurements of the Mastoid Triangle and Volume of the Mastoid Air Cell System Using Classical and Machine Learning Methods

Sasani,

Etli,

Tastekin

et al. 2023

Am J Forensic Med Pathol

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Previous studies on the sexual dimorphism of the mastoid triangle have typically focused on linear and area measurements. No studies in the literature have used mastoid air cell system volume measurements for direct anthropological or forensic sex determination. The aims of this study were to investigate the applicability of mastoid air cell system volume measurements and mastoid triangle measurements separately and combined for sex estimation, and to determine the accuracy of sex estimation rates using machine learning algorithms and discriminant function analysis of these data. On 200 computed tomography images, the distances constituting the edges of the mastoid triangle were measured, and the area was calculated using these measurements. A region-growing algorithm was used to determine the volume of the mastoid air cell system. The univariate sex determination accuracy was calculated for all parameters. Stepwise discriminant function analysis was performed for sex estimation. Multiple machine learning methods have also been used. All measurements of the mastoid triangle and volumes of the mastoid air cell system were higher in males than in females. The accurate sex estimation rate was determined to be 79.5% using stepwise discriminant function analysis and 88.5% using machine learning methods.

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Section: Discussionmentioning

confidence: 99%

Sex Estimation From Measurements of the Mastoid Triangle and Volume of the Mastoid Air Cell System Using Classical and Machine Learning Methods

Sasani,

Etli,

Tastekin

et al. 2023

Am J Forensic Med Pathol

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“…The proposed technique produced good results with a mean absolute error of 5.15 years and a concordance correlation value of 0.80. In another research [19], multiclass Support Vector Machine (SVM) for age estimation and a Library for Support Vector Machines (LIBSVM) for the prediction of gender is used. A total of 1142 X-Rays of teeth were used for research and experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although, the results are reasonable, however, 814 scans could not ensure robustness of the model. In the research [19], SVM has been used for age and gender estimation. Model has been evaluated on a total of 1142 images, with achieved accuracy as 96%.…”

Section: Comparison With the State-of-the-art Studiesmentioning

confidence: 99%

CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

Batool¹,

Gilanie²

2023

Computers, Materials &Amp; Continua

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Automated and autonomous decisions of image classification systems have essential applicability in this modern age even. Image-based decisions are commonly taken through explicit or auto-feature engineering of images. In forensic radiology, auto decisions based on images significantly affect the automation of various tasks. This study aims to assist forensic radiology in its biological profile estimation when only bones are left. A benchmarked dataset Radiology Society of North America (RSNA) has been used for research and experiments. Additionally, a locally developed dataset has also been used for research and experiments to cross-validate the results. A Convolutional Neural Network (CNN)-based model named computer vision and image processing-net (CVIP-Net) has been proposed to learn and classify image features. Experiments have also been performed on state-of-the-art pertained models, which are alex_net, inceptionv_3, google_net, Residual Network (resnet)_50, and Visual Geometry Group (VGG)-19. Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender. The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy (98.90%), specificity (97.99%), sensitivity (99.34%), and Area under the Curve (AUC)-value (0.99) on the locally developed dataset to detect age. The classification rates of the proposed model for gender estimation were 99.57%, 97.67%, 98.99%, and 0.98, achieved in terms of accuracy, specificity, sensitivity, and AUC-value, respectively, on the local dataset. The classification rates of the proposed model for age estimation were 96.80%, 96.80%, 97.03%, and 0.99 achieved in terms of accuracy, specificity, sensitivity, and AUC-value, respectively, on the RSNA dataset.

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“…Tasks in speech recognition or image recognition can take minutes rather than hours compared with manual identification by human experts [ 18 ]. Deep Learning’s application in forensic medicine has been explored over recent years due to its advantages of accuracy and precision in age and gender estimation [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…A previous study utilized X–ray images of teeth along with Machine Learning techniques to achieve 97% accuracy in age estimation, which implies that Machine Learning can be applied effectively in forensic investigations to obtain accurate and quick results [ 19 ]. Gender determination on panoramic radiographs using neural networks also exhibited good gender prediction compared with other methods, such as logistic and discriminant analysis [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks

et al. 2023

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The present study explores the efficacy of Machine Learning and Artificial Neural Networks in age assessment using the root length of the second and third molar teeth. A dataset of 1000 panoramic radiographs with intact second and third molars ranging from 12 to 25 years was archived. The length of the mesial and distal roots was measured using ImageJ software. The dataset was classified in three ways based on the age distribution: 2–Class, 3–Class, and 5–Class. We used Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression models to train, test, and analyze the root length measurements. The mesial root of the third molar on the right side was a good predictor of age. The SVM showed the highest accuracy of 86.4% for 2–class, 66% for 3–class, and 42.8% for 5–Class. The RF showed the highest accuracy of 47.6% for 5–Class. Overall the present study demonstrated that the Deep Learning model (fully connected model) performed better than the Machine Learning models, and the mesial root length of the right third molar was a good predictor of age. Additionally, a combination of different root lengths could be informative while building a Machine Learning model.

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

Cited by 22 publications

References 26 publications

Sex Estimation From Measurements of the Mastoid Triangle and Volume of the Mastoid Air Cell System Using Classical and Machine Learning Methods

Sex Estimation From Measurements of the Mastoid Triangle and Volume of the Mastoid Air Cell System Using Classical and Machine Learning Methods

CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks

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