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Can deep learning identify humans by automatically constructing a database with dental panoramic radiographs?

Hye-Ran Choi¹, Thomhert Suprapto Siadari², Dong-Yub Ko²

¹Department of Advanced General Dentistry, Inje University Sanggye Paik Hospital, Seoul, Korea.

|October 24, 2024

Summary

This study introduces a deep learning method for human identification using dental panoramic radiographs (DPRs). It accurately matches postmortem and antemortem dental images, improving identification success rates, especially for women.

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Area of Science:

Forensic Odontology
Artificial Intelligence
Biometrics

Background:

Human identification is crucial in forensic science.
Dental records, specifically dental panoramic radiographs (DPRs), offer a unique identifier.
Traditional methods for matching antemortem (AM) and postmortem (PM) dental records can be time-consuming.

Purpose of the Study:

To develop and evaluate a novel deep learning-based method for human identification using dentition changes.
To assess the effectiveness of convolutional neural networks (CNNs) in matching AM and PM dental panoramic radiographs.
To analyze the impact of imaging time intervals and sex on identification accuracy.

Main Methods:

A dataset of 1,029 paired AM-PM DPRs from adults aged 20-49 was utilized.
A deep learning model was trained to recognize dentition changes and calculate similarity scores.
Candidate groups (CGs) were generated based on similarity, and matched ranks were analyzed.

Main Results:

The method achieved high success rates in matching AM to PM DPRs, with top-20% extraction yielding 83.2% accuracy.
Success rates were significantly higher for women (97.2% for top 20%) compared to men (71.3%).
A significant difference in similarity scores was observed based on an average imaging time interval of 17.7 years.

Conclusions:

Deep learning applied to dental panoramic radiographs is a highly effective method for human identification.
The proposed technique significantly reduces the size of candidate groups, streamlining the identification process.
The study highlights the potential of AI in forensic odontology for accurate and efficient identification.