Demirjian Metodu Farklı Ülkelerde Yaş Tayininde Kullanılabilir mi?

Yaşar, Zehtiye Füsun; Büken, Erhan; Tekindal, Mustafa Agâh

doi:10.17986/blm.2016323747

Cited by 3 publications

(2 citation statements)

References 26 publications

(29 reference statements)

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“…In the physical and chemical analysis method, age estimation is facilitated by evaluating changes in ion levels in the teeth. The effective utilization of these methods holds significant importance in forensic medicine practices and the field of forensic dentistry (7).…”

Section: Dental Age Estimation Methodsmentioning

confidence: 99%

Yeni̇ Nesi̇l Dental Yaş Tayi̇ni̇ Yöntemleri̇ne Güncel Bi̇r Bakiş: Bi̇r Derleme

POLAT

2023

HRU Int J Dent Oral Res

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In forensic odontology, dental findings play a significant role in determining age and gender. Due to their anatomical structures and durability, teeth have a critical function in identification, even when exposed to mechanical, chemical, and physical effects. However, most of the estimated dental age determination methods are generally invasive, costly, and require procedures like tooth extraction. Additionally, misinterpretations can occur in 2-dimensional imaging due to distortions and magnifications. Recently, three-dimensional imaging technologies, especially Cone Beam Computed Tomography (CBCT), Computed Tomography (CT), and micro-CT, are being used to investigate the potential relationship between the volumetric data of the tooth and pulp and age. CBCT allows researchers to comprehensively evaluate pulp changes and explain the relationship between chronological age and pulp volume. With the 3D images obtained by CBCT, the volumetric measurements of the tooth and pulp can be objectively evaluated and can be used as a criterion for estimated dental age. This review aims to identify, investigate, and summarize the most reliable, practical, and accurate methods for age estimation based on current data and methods.

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Section: Dental Age Estimation Methodsmentioning

confidence: 99%

Yeni̇ Nesi̇l Dental Yaş Tayi̇ni̇ Yöntemleri̇ne Güncel Bi̇r Bakiş: Bi̇r Derleme

POLAT

2023

HRU Int J Dent Oral Res

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“…The end and beginning of the dentition periods provide considerable information on age estimation. Especially six years of age, which is the beginning of the permanent dentition period, and 14 years, which is considered to be the end of the complex dentition period in infancy and childhood, are reliable periods used in age estimation [24]. Age estimation may be performed to 20, which is the end of the completion period of the formation of the third molar teeth.…”

Section: Introductionmentioning

confidence: 99%

Age-Net: An Advanced Hybrid Deep Learning Model for Age Estimation Using Orthopantomograph Images

Baydogan,

Baybars,

Tuncer

2023

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Forensic odontology, recognized as a fundamental and reliable technique in human identification, frequently employs orthopantomograph images in dental biometry. Despite the introduction of various techniques for age and identity estimation, the accurate and rapid interpretation of these images remains challenging. Manual methods, currently employed by forensic experts, present numerous limitations including time consumption, human error, and challenges in handling large data sets. Addressing these limitations, this study proposes a computer-aided hybrid age detection system, Age-Net, leveraging artificial intelligence. A total of 933 orthopantomograph images, categorized into three classes, were collected from Firat University Hospital for this study. These images were subsequently resized to be compatible with pre-trained Convolutional Neural Networks (CNNs) models, such as AlexNet, ResNet50, VGG16, SqueezeNet, EfficientNetB0, DenseNet201, and ResNet18. Following the extraction of feature vectors from these images, algorithms including Naive Bayes (NB), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), XGBoost Algorithm, Support Vector Machine (SVM), Decision Tree (DT), and Linear Discriminant (LD) were implemented for detection. The use of an array of feature extraction models and algorithms aimed to best represent the features of the dataset, thereby enhancing classification performance. The proposed system's efficacy was assessed and validated using the 5-fold cross-validation test technique and statistical Friedman test. Of all models, the EfficientNetB0-SVM hybrid model demonstrated superior performance, achieving highest accuracy, precision, sensitivity, F-score, and AUC ratios of 0.846, 0.850, 0.846, 0.846, and 0.970, respectively. This hybrid detection system, Age-Net, is projected to provide time and cost benefits to forensic experts in their clinical studies. However, the data utilized in this study are limited to specific age groups. Future research could expand the number of age groups and data to observe potential enhancements in the system's success rate.

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