A New Approach Based on Metaheuristic Optimization Using Chaotic Functional Connectivity Matrices and Fractal Dimension Analysis for AI-Driven Detection of Orthodontic Growth and Development Stage

Accurate identification of growth and development stages is critical for orthodontic diagnosis, treatment planning, and post-treatment retention. While hand–wrist radiographs are the traditional gold standard, the associated radiation exposure necessitates alternative imaging methods. Lateral cephal...

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Vydáno v:Fractal and fractional Ročník 9; číslo 3; s. 148
Hlavní autoři: Cicek, Orhan, Özçelik, Yusuf Bahri, Altan, Aytaç
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 26.02.2025
Témata:
Accuracy
Algorithms
Artificial intelligence
Automation
cervical vertebrae
chaotic and fractal functional connectivity matrices
Complexity
Diagnosis
Effectiveness
Fast Fourier transformations
Fourier transforms
Fractal analysis
fractal dimension analysis
Fractal geometry
Fractals
growth and development
Heuristic methods
Machine learning
Medical diagnosis
metaheuristic Puma optimizer algorithm
Methods
Morphology
Nonlinear dynamics
Optimization
Optimization techniques
Orthodontics
Planning
Radiation
Radiation effects
Radiographs
Reproducibility
Vertebrae
Wrist
ISSN:2504-3110, 2504-3110
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Shrnutí:Accurate identification of growth and development stages is critical for orthodontic diagnosis, treatment planning, and post-treatment retention. While hand–wrist radiographs are the traditional gold standard, the associated radiation exposure necessitates alternative imaging methods. Lateral cephalometric radiographs, particularly the maturation stages of the second, third, and fourth cervical vertebrae (C2, C3, and C4), have emerged as a promising alternative. However, the nonlinear dynamics of these images pose significant challenges for reliable detection. This study presents a novel approach that integrates chaotic functional connectivity (FC) matrices and fractal dimension analysis to address these challenges. The fractal dimensions of C2, C3, and C4 vertebrae were calculated from 945 lateral cephalometric radiographs using three methods: fast Fourier transform (FFT), box counting, and a pre-processed FFT variant. These results were used to construct chaotic FC matrices based on correlations between the calculated fractal dimensions. To effectively model the nonlinear dynamics, chaotic maps were generated, representing a significant advance over traditional methods. Feature selection was performed using a wrapper-based approach combining k-nearest neighbors (kNN) and the Puma optimization algorithm, which efficiently handles the chaotic and computationally complex nature of cervical vertebrae images. This selection minimized the number of features while maintaining high classification performance. The resulting AI-driven model was validated with 10-fold cross-validation and demonstrated high accuracy in identifying growth stages. Our results highlight the effectiveness of integrating chaotic FC matrices and AI in orthodontic practice. The proposed model, with its low computational complexity, successfully handles the nonlinear dynamics in C2, C3, and C4 vertebral images, enabling accurate detection of growth and developmental stages. This work represents a significant step in the detection of growth and development stages and provides a practical and effective solution for future orthodontic diagnosis.
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ISSN:2504-3110
2504-3110
DOI:10.3390/fractalfract9030148