With or without human interference for precise age estimation based on machine learning?

Han, Mengqi; Du, Shaoyi; Ge, Yuyan; Dong, Zhen; Chi, Yuting; Li, Hong; Yang, Jing; Yang, Yang; Xin, Jingmin; Chen, Teng; Zheng, Nanning; Guo, Yucheng

doi:10.1007/s00414-022-02796-z

Cited by 19 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike end-to-end estimation schemes with no human intervention and automatic extraction of dental features for age estimation using CNN algorithms 22 , this study manually determines the staging and then uses machine learning algorithms to continuously iterate to obtain the most appropriate age estimation model. This estimation process can be accomplished using a lightweight computer terminal, which eliminates the tediousness of using a large server for model training.…”

Section: Discussionmentioning

confidence: 99%

Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population

Shan

Sun

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Age estimation based on the mineralized morphology of teeth is one of the important elements of forensic anthropology. To explore the most suitable age estimation protocol for adolescents in the South China population, 1477 panoramic radiograph images of people aged 2–18 years in the South were collected and staged by the Demirjian mineralization staging method. The dental ages were estimated using the parameters of the Demirjian and Willems. Mathematical optimization and machine learning optimization were also performed in the data processing process in an attempt to obtain a more accurate model. The results show that the Willems method was more accurate in the dental age estimation of the southern China population and the model can be further optimized by reassigning the model through a nonintercept regression method. The machine learning model presented excellent results in terms of the efficacy comparison results with the traditional mathematical model, and the machine learning model under the boosting framework, such as gradient boosting decision tree (GBDT), significantly reduced the error in dental age estimation compared to the traditional mathematical method. This machine learning processing method based on traditional estimation data can effectively reduce the error of dental age estimation while saving arithmetic power. This study demonstrates the effectiveness of the GBDT algorithm in optimizing forensic age estimation models and provides a reference for other regions to use this parameter for age estimation model establishment, and the lightweight nature of machine learning offers the possibility of widespread forensic anthropological age estimation.

show abstract

Section: Discussionmentioning

confidence: 99%

Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population

Shan

Sun

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Unlike end-to-end estimation schemes with no human intervention and automatic extraction of dental features for age estimation using CNN algorithms 20 , this study manually determines the staging and then uses machine learning algorithms to continuously iterate to obtain the most appropriate age estimation model, and this estimation process can be accomplished using a lightweight computer terminal, which eliminates the tediousness of using a large server for model training. In comparison, the MAE obtained by the GBDT algorithm (< 0.5) is also lower than that of the end-to-end solution (0.83).…”

Section: Discussionmentioning

confidence: 99%

Boosting Algorithm for Optimizing Morphological Dental Age Estimation Method: A Southern China Population Study

Shan

Sun

et al. 2022

Preprint

View full text Add to dashboard Cite

Age estimation based on the mineralized morphology of teeth is one of the important elements of forensic anthropology. To explore the most suitable age assessment protocol for adolescents in the South China population, 1477 panoramic radiographs images of people aged 2–18 years in the South were collected and staged by the Demirjian mineralization staging method. The dental age were estimated using the parameters of the Demirjian and Willems. Mathematical optimization and machine learning optimization were also performed in the data processing process in an attempt to obtain a more accurate model. The results show that Willems' method is more accurate in estimating the dental age of the South China population, while the model can be further optimized by re-assigning the model through a non-intercept regression method. The machine learning model presented excellent results in terms of the efficacy comparison results with the traditional mathematical model, and the machine learning model under the Boosting framework such as Gradient Boosting Decision Tree (GBDT) significantly reduced the error in dental age estimation compared to the traditional mathematical method. This machine learning processing method based on traditional assessment data can effectively reduce the error of assessment while saving arithmetic power. This study demonstrates the effectiveness of the GBDT algorithm in optimizing forensic age estimation models and provides a reference for other regions to use this scheme for age assessment model architecture, also the lightweight nature of machine learning offers the possibility of widespread forensic anthropological age estimation.

show abstract

“…To overcome these problems, various studies have been conducted to develop artificial intelligence (AI) models that can automatically extract the features of teeth via deep learning using orthopantomograms (OPGs) and estimate the age based on the obtained feature values 5,[7][8][9] . Among various machine-learning-based methods for extracting feature values from medical images, a deep learning analytical method based on a convolutional neural network (CNN) has proved highly effective in diagnosing subjects' symptoms based on medical images or estimating the age of bones 10 .…”

Section: Variational Autoencoder-based Estimation Of Chronological Ag...mentioning

confidence: 99%

Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth

Joo

Jung

2023

Sci Rep

View full text Add to dashboard Cite

This study led to the development of a variational autoencoder (VAE) for estimating the chronological age of subjects using feature values extracted from their teeth. Further, it determined how given teeth images affected the estimation accuracy. The developed VAE was trained with the first molar and canine tooth images, and a parallel VAE structure was further constructed to extract common features shared by the two types of teeth more effectively. The encoder of the VAE was combined with a regression model to estimate the age. To determine which parts of the tooth images were more or less important when estimating age, a method of visualizing the obtained regression coefficient using the decoder of the VAE was developed. The developed age estimation model was trained using data from 910 individuals aged 10–79. This model showed a median absolute error (MAE) of 6.99 years, demonstrating its ability to estimate age accurately. Furthermore, this method of visualizing the influence of particular parts of tooth images on the accuracy of age estimation using a decoder is expected to provide novel insights for future research on explainable artificial intelligence.

show abstract

With or without human interference for precise age estimation based on machine learning?

Cited by 19 publications

References 35 publications

Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population

Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population

Boosting Algorithm for Optimizing Morphological Dental Age Estimation Method: A Southern China Population Study

Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth

Contact Info

Product

Resources

About