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image of CLPr_in_ML: Cleft Lip and Palate Reconstructed Features with Machine Learning

Abstract

Background

Cleft lip and palate are two of the most common craniofacial congenital malformations in humans. It influences tens of millions of patients worldwide. The hazards of this disease are multifaceted, extending beyond the obvious facial malformation to encompass physiological functions, oral health, psychological well-being, and social aspects.

Objective

The primary objective of our study is to demonstrate the importance of imaging in detecting cleft lip and palate. By observing the morphological and structural abnormalities involving the lip and palate through imaging methods, this study aims to establish imaging as the primary diagnostic approach for this disease.

Methods

In this work, we proposed a novel model to analyze unilateral complete cleft lip and palate after velopharyngeal closure and non-left lip and palate patients from the Department of Stomatology of Xuzhou First People's Hospital, Conical Beam CT (CBCT) images in silicon. In order to demonstrate the generalization, the simulated dataset was constructed using the random disturbance factor, which is from the actual dataset. We extracted several raw features from CBCT images in detail. Then, we proposed a novel feature reconstruction method, including six types of reconstructed factors, to reconstruct the existing features. Then, the reconstructed features weretrained with machine learning algorithms. Finally, the testing and independent data model was utilized to analyze the performance of this work.

Results

By comparing different operator features, the min operator, max operator, average operator, and all operators can achieve good performances in both the testing set and the independent set.

Conclusion

With the different operator features, the majority of classification models, including Gradient Boosting, Hist Gradient Boosting, Multilayer Perceptron, lightGBM, and broadened learning, classification algorithms can get the well-performances in the selected reconstructed feature operators.

© 2024 Bentham Science Publishers
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/content/journals/cbio/10.2174/0115748936330499240909082529
2024-10-09
2024-11-23

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/content/journals/cbio/10.2174/0115748936330499240909082529
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CLPr_in_ML: Cleft Lip and Palate Reconstructed Features with Machine Learning
Bentham Science Publishers ; https://doi.org/10.2174/0115748936330499240909082529
/content/journals/cbio/10.2174/0115748936330499240909082529
/content/journals/cbio/10.2174/0115748936330499240909082529
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  • Article Type:     Research Article
Keywords: craniofacial congenital ; palate ; classification ; conical beam CT ; random disturbance factor ; reconstructed features ; Cleft lip ; machine learning

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