Assessing the accuracy of Cameriere’s Indian-specific formula for age estimation on right and left sides of orthopantomogram

Vadla, Purnima; Surekha, R.; Guttikonda, Venkateswara Rao; Deepthi, G; Naveen, Sayani; Kumar, Chippalapally Arun

doi:10.1186/s41935-020-00183-6

Cited by 10 publications

(8 citation statements)

References 12 publications

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“…This outcome indicates that mandible ramus exhibits better sexual dimorphism. In 2020, Poornima Vadla et al [ 17 ] introduced a technique based on permanent mandible teeth of the left-sided jaw. Their study focused on estimating age with high accuracy based on the Camerier method applied from an Indian-specific formula on the left and right sides of mandibular teeth.…”

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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“…This study utilized the left quadrant as no statistical difference between growth rate of dentition on right and side was documented in the literature [ 39 ]. Furthermore, Vadla et al [ 2 ] concluded that the left side of panoramic radiographs showed “superior results” in comparison to the left side. Dental age was then estimated by employing Cameriere’s (Italian) linear Regression Formula [ 8 ]: Age = 8.971 + 0.375g + 1.631(x 5 ) + 0.674 (N 0 ) − 1.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The estimation of an individual’s age is a fundamental component of forensic odontology, which is a branch of dentistry that examines dental evidence [ 1 , 2 ]. The demand for accurate age estimation is imperative for issues pertaining to illegal immigration (child labour), legal medicine (human trafficking/kidnapping), orthodontic treatment, criminal cases, adoption of individuals without birth certification and natural or mass disasters [ 2 , 3 , 4 ]. Furthermore, the South African “Child Justice Inter-Departmental Annual Report—2019/2020” stated that approximately 2984 individuals aged 17 years were awaiting trial within South Africa and in accordance with the South African Bill of Rights and Children’s Act—a “child” is an individual under the age of 18 years.…”

Section: Introductionmentioning

confidence: 99%

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The Application of the Cameriere’s Methodologies for Dental Age Estimation in a Select KwaZulu-Natal Population of South Africa

2022

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Background: The estimation of an individual’s age is a fundamental component of forensic odontology. Literary reports found that the efficiency of Cameriere methodology for age estimation varied among many population groups. Therefore, this study aimed to determine the applicability of the Cameriere methods to a select South African population of the KwaZulu-Natal (KZN) province. Materials and Methods: This cross-sectional retrospective study was conducted on 840 digital panoramic radiographs that met the inclusion criteria. Dental maturity was determined through the morphometric analysis of the seven left permanent mandibular and maxillary teeth in accordance with Cameriere et al. (2006). Moreover, the dental age was also calculated using the South African Black Bayesian formulae of the Cameriere method by Angelakopoulos et al. (2019). The paired sample t-test or Wilcoxon’s signed rank test assessed the significant difference between the chronological age and estimated dental age for the various formulae. A p-value < 0.05 was considered to be statistically significant. Results: The Cameriere et al. (2006) Italian formula and the South African Black Bayesian formulae of the Cameriere method by Angelakopoulos et al. (2019) underestimated and overestimated age in the South African Black and Indian population groups of the KZN province, respectively. Therefore, the authors generated a novel population-specific regression formulae (including and excluding third molars) using “step-wise regression analysis” and a “best-fit model” for the South African Black and Indian population groups of KZN. Conclusion: This study recommends that the population-specific formulae generated in this study be utilized in the KZN population to improve the accuracy of dental age estimation within this region.

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“…The various types of CNN are GoogLeNet [9], DenseNet [13], and ResNets are implemented. The proposed work uses ResNets [22] in DCNN. Human age is determined by facial structure [14,29] and it may be different from that detected using our human eye.…”

Section: F1-scorementioning

confidence: 99%

An Intelligent Human Age Prediction from Face Image Framework Based on Deep Learning Algorithms

Sathyavathi¹,

Baskaran²

2023

ITC

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Age prediction is the task of extracting features from the human face image. Human aging factors can be expressed as multifactorial, gradual, time-dependent, physical, and biological damage. Attributes are extracted from a face image, and the aging factor depends on cells, tissues, and all living organisms. Human age prediction is distinct from chronological age prediction. Each human’s biological identity has unique characteristics. Age prediction depends on the maturity process of organs, other tissues, and cells. Many research works have been done on age classification using various techniques from human face images. It is a difficult task to the analysis of facial appearance. Issues in the existing algorithm are inefficient and require more computation time and storage space. To address these issues, this paper proposed a Deep convolutional neural network (DCNN) with a Cuckoo search algorithm (DCNN-CS). In this proposed work, DCNN-CS produces an effective age prediction from the human face image within a minimum execution time, handling a large dataset. The accuracy rate of the convolutional neural network (CNN) got 81.32, the Deep Neural Network (DNN) got 82.34, the Long short-term memory (LSTM) got 88.12, and the proposed work SLSTM-DNN got 91.45.

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Assessing the accuracy of Cameriere’s Indian-specific formula for age estimation on right and left sides of orthopantomogram

Cited by 10 publications

References 12 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

The Application of the Cameriere’s Methodologies for Dental Age Estimation in a Select KwaZulu-Natal Population of South Africa

An Intelligent Human Age Prediction from Face Image Framework Based on Deep Learning Algorithms

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