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Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

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Leverage Effective Deep Learning Searching Method for Forensic Age Estimation.

Zhi-Yong Zhang^1,2,3, Chun-Xia Yan², Qiao-Mei Min³

¹Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an 710004, China.

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|July 27, 2024

Summary

This summary is machine-generated.

This study introduces AGENet and AGE-SPOS, novel deep neural network models for precise dental age estimation using orthopantomograms. These AI approaches significantly improve accuracy, especially for adults, advancing forensic medicine.

Keywords:

Neural Architecture Search (NAS)age estimation deep neural network (DNN)orthopantomograms (OPGs)

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Area of Science:

Forensic Odontology
Artificial Intelligence in Medicine
Radiology

Background:

Conventional dental age estimation methods lack precision, particularly for adults.
Orthopantomograms (OPGs) offer rich data for age assessment.
Need for accurate and efficient age estimation in forensic contexts.

Purpose of the Study:

To develop and validate novel deep learning models for accurate dental age estimation using OPGs.
To explore optimal neural network architectures for analyzing dental data.
To create both high-performance and lightweight models for forensic applications.

Main Methods:

Creation of a large-scale dental dataset (27,957 OPGs) with verified age annotations.
Analysis of neural network components: depth, kernel size, multi-branch architecture, and feature reuse.
Development and evaluation of two deep neural network models: AGENet (high-performance) and AGE-SPOS (lightweight).

Main Results:

AGENet achieved a Mean Absolute Error (MAE) of 1.70 years, outperforming Inception-v4 and reducing computational cost by 2.7x.
The lightweight AGE-SPOS model attained an MAE of 1.80 years with significantly fewer computations than MobileNetV2.
Both models demonstrated superior effectiveness and accuracy in forensic age estimation tasks.

Conclusions:

The proposed deep neural network searching method provides effective forensic age estimation from OPGs.
AGENet and AGE-SPOS represent significant advancements in accuracy and computational efficiency for dental age prediction.
These findings have substantial implications for improving age estimation techniques in oral and maxillofacial imaging.