Proposal of dental demineralization diagnosis with OCT echo based on multiscale entropy analysis

Ziqi Peng; Seiroh Okaneya; Hongzi Bai; Chuangxing Wu; Bei Liu; Tatsuo Shiina; Ziqi Peng; Seiroh Okaneya; Hongzi Bai; Chuangxing Wu; Bei Liu; Tatsuo Shiina

doi:10.3934/mbe.2024195

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 4421-4439. doi: 10.3934/mbe.2024195

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Proposal of dental demineralization diagnosis with OCT echo based on multiscale entropy analysis

1.
College of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China
2.
Hunan Province Key Laboratory of Photoelectric Information Integration and Optical Manufacturing Technology, Changde 415000, China
3.
Phoenixdent Co. Ltd., Tokyo 1580091, Japan
4.
Graduate School of Engineering, Chiba University, Chiba 2638522, Japan

Academic Editor: Qianqian Song

Received: 15 December 2023 Revised: 15 February 2024 Accepted: 18 February 2024 Published: 27 February 2024

Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis. Three multiscale entropy analysis methods were used to extract features from the OCT one-dimensional echo signal of normal and demineralized teeth, and a probabilistic neural network (PNN) was used for dental demineralization diagnosis. By comparing diagnostic efficiency, diagnostic speed, and parameter optimization dependency, the multiscale dispersion entropy-PNN (MDE-PNN) method was found to have comprehensive advantages in dental demineralization diagnosis with a diagnostic efficiency of 0.9397. Compared with optical feature-based dental demineralization diagnosis methods, the entropy features-based analysis had better feature separability and higher diagnostic efficiency, and showed its potential in dental demineralization diagnosis with OCT.
- diagnosis of dental demineralization,
- optical coherence tomography,
- multiscale dispersion entropy,
- probabilistic neural network
Citation: Ziqi Peng, Seiroh Okaneya, Hongzi Bai, Chuangxing Wu, Bei Liu, Tatsuo Shiina. Proposal of dental demineralization diagnosis with OCT echo based on multiscale entropy analysis[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 4421-4439. doi: 10.3934/mbe.2024195

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Abstract

Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis. Three multiscale entropy analysis methods were used to extract features from the OCT one-dimensional echo signal of normal and demineralized teeth, and a probabilistic neural network (PNN) was used for dental demineralization diagnosis. By comparing diagnostic efficiency, diagnostic speed, and parameter optimization dependency, the multiscale dispersion entropy-PNN (MDE-PNN) method was found to have comprehensive advantages in dental demineralization diagnosis with a diagnostic efficiency of 0.9397. Compared with optical feature-based dental demineralization diagnosis methods, the entropy features-based analysis had better feature separability and higher diagnostic efficiency, and showed its potential in dental demineralization diagnosis with OCT.

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