Human Age Estimation Using Deep Convolutional Neural Network based on Dental Images (Orthopantomogram)

Sathyavathi, S.; Baskaran, K.R.

doi:10.1080/03772063.2023.2165177

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…A study on age determination was conducted by Sathyavathi and Baskaran [41]. In the study, 3508 Orthopantomogram (OPG) images collected from individuals aged 10-30 were used.…”

Section: Related Workmentioning

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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“…A study on age determination was conducted by Sathyavathi and Baskaran [41]. In the study, 3508 Orthopantomogram (OPG) images collected from individuals aged 10-30 were used.…”

Section: Related Workmentioning

confidence: 99%

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

Baydogan,

Baybars,

Tuncer

2023

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“…In legal investigations, orthodontic planning, and age-sensitive therapeutic procedures, dental age assessment is essential. Traditional techniques are sensitive to subjectivity and variability among observers since they rely on measurements being taken manually and visual inspection [1]. A strong alternative is provided by deep neural networks with recurrent connections, which can detect temporal relationships in visual sequences.…”

Section: Research Backgroundmentioning

confidence: 99%

Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

B. Hemalatha

2023

J. Adv. Zool.

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The First Chapter's introduction discussed the ability to determine a person's gender and dental age with great accuracy and efficiency is made possible by this technology. It has also done a study that aims to leverage the groundbreaking advantages of deep learning in the dental age and gender evaluation by providing an accurate and automated approach that goes beyond the constraints of traditional methods. The Second Chapter's Literature Review explained Deep Learning Applications in Dental Radiography and Traditional Methods for Dental Age and Gender Assessment and Datasets and Annotations for Dental Radiographs. It has also done Temporal Dependencies in Dental Radiographs. The Third Chapter Methodology discussed that dental radiography data contains rich environmental information that necessitates a nuanced comprehension; this study employs an interpretivist research philosophy. It has also been done to examine pre-existing ideas and models in the context of tooth age and gender evaluation, a deductive approach is used in this study.

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Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs — a systematic review

Niño-Sandoval,

Doria-Martinez,

Escobar

et al. 2024

Int J Legal Med

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Human Age Estimation Using Deep Convolutional Neural Network based on Dental Images (Orthopantomogram)

Cited by 6 publications

References 10 publications

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

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

Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs — a systematic review

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