Dental Age Estimation in East Asian Population with Least Squares Regression

Tao, Junli; Chen, Mufan; Wang, Jian; Liu, Lin; Hassanien, Aboul Ella; Xiao, Kai

doi:10.1007/978-3-319-74690-6_64

“…A study [11] created a machine learning model to predict the DA. The dataset consisted of 1697 panoramic radiographs (820 boys and 877 girls), with ages ranging from 10 to 19.…”

Section: Related Studiesmentioning

confidence: 99%

Predictive Artificial Intelligence Model for Detecting Dental Age Using Panoramic Radiograph Images

Aljameel

¹

,

Althumairy

²

,

Albassam

³

et al. 2023

BDCC

View full text Add to dashboard Cite

Predicting dental development in individuals, especially children, is important in evaluating dental maturity and determining the factors that influence the development of teeth and growth of jaws. Dental development can be accelerated in patients with an accelerated skeletal growth rate and can be related to the skeletal growth pattern as a child. The dental age (DA) of an individual is essential to the dentist for planning treatment in relation to maxillofacial growth. A deep-learning-based regression model was developed in this study using panoramic radiograph images to predict DA. The dataset included 529 samples of panoramic radiographs collected from the dental hospital at Imam Abdulrahman Bin Faisal university in Saudi Arabia. Different deep learning methods were applied to implement the model, including Xception, VGG16, DenseNet121, and ResNet50. The results indicated that the Xception model had the best performance, with an error rate of 1.417 for the 6–11 age group. The proposed model can assist the dentist in determining the appropriate treatment for patients based on their DA rather than their chronological age.

show abstract

“…In recent years, Machine Learning/Deep Learning techniques have been widely used to identify patterns in complex data such as clinical imaging, genomics, bioimaging, and phenotypic data [9][10][11][12][13]. Machine learning algorithms for example, NN, SVM, KNN, Decision Tree, displayed promising ability in prediction and classification [14][15][16][17][18][19][20][21][22][23] including estimation of bone age [24,25]. The medical and biological datasets are increasing rapidly.…”

Section: Introductionmentioning

confidence: 99%

Hybrid HCNN-KNN Transfer Learning Model Enhances Age Estimation Accuracy in Orthopantomography

Sharifonnasabi¹,

Jhanjhi²,

Jacob³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilise for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- one year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers and transfer learning techniques to enhance the accuracy of age estimation using OPGs from one year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbours (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimise the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of one year old, six months, three months and one-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.

show abstract

“…In recent years, Machine Learning/Deep Learning techniques have been widely used to identify patterns in complex data such as clinical imaging, genomics, bioimaging, and phenotypic data [13][14][15][16][17]. Machine learning algorithms for example, NN, SVM, KNN, Decision Tree, displayed promising ability in prediction and classification [18][19][20][21][22][23][24][25][26][27][28] including estimation of bone age [29,30]. The medical and biological datasets are increasing rapidly.…”

Section: Introductionmentioning

confidence: 99%

Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography

Sharifonnasabi¹,

Jhanjhi²,

John³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilise for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- one year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers and transfer learning techniques to enhance the accuracy of age estimation using OPGs from one year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbours (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimise the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of one year old, six months, three months and one-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.

show abstract

Dental Age Estimation in East Asian Population with Least Squares Regression

Cited by 8 publications

References 24 publications

Predictive Artificial Intelligence Model for Detecting Dental Age Using Panoramic Radiograph Images

Predictive Artificial Intelligence Model for Detecting Dental Age Using Panoramic Radiograph Images

Hybrid HCNN-KNN Transfer Learning Model Enhances Age Estimation Accuracy in Orthopantomography

Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography

Contact Info

Product

Resources

About