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Teeth

Teeth

The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...

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OPG-based dental age estimation using a data-technical exploration of deep learning techniques.

Barkın Büyükçakır¹, Jeroen Bertels¹, Peter Claes¹

¹ESAT, Center for Processing Speech and Images, KU Leuven, Leuven, Belgium.

Journal of Forensic Sciences

|January 31, 2024

Summary

Optimizing convolutional neural network hyperparameters, like batch size, significantly improves automated dental age estimation from orthopantomograms (OPGs). Larger batch sizes generally enhance accuracy in forensic age assessment using deep learning.

Keywords:

OPG convolutional neural network dental age estimation hyperparameter optimization

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Area of Science:

Forensic Science
Computer Science
Radiology

Background:

Manual dental age estimation faces challenges like tedium and interobserver variability.
Automated deep learning methods for age estimation struggle with data scarcity and training complexities.

Purpose of the Study:

To investigate the impact of convolutional neural network (CNN) hyperparameters on dental age estimation accuracy.
To evaluate model complexity, batch size, and sample quantity effects on age estimation from orthopantomograms (OPGs).

Main Methods:

Cross-validation of EfficientNet-B4, DenseNet-201, and MobileNet V3 models on 3896 OPGs.
Training with batch sizes ranging from 10 to 160 and using random data subsets.
Analysis of hyperparameter tuning effects on mean absolute error (MAE).

Main Results:

EfficientNet-B4 achieved the lowest MAE of 0.562 years with a batch size of 160 on the full dataset.
Increasing batch size improved performance for EfficientNet-B4 and DenseNet-201, while MobileNet V3 peaked at batch size 40.
Training with complete datasets outperformed reduced sample sizes, highlighting the importance of data quantity.

Conclusions:

Hyperparameter optimization is crucial for effective deep learning-based dental age estimation.
Tailored training methodologies and sufficient data are key to achieving accurate forensic age assessment.
This study advances automated age estimation, demonstrating the potential of optimized CNNs.