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Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis

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Abstract

Objectives

We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis.

Materials and methods

Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation map** method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics.

Results

The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores.

Conclusion

The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

The authors gratefully acknowledge the financial support of the Key Research General Project of Department of Science and Technology of Jiangxi Province (20212BBG73006); the Cross-funding Project of Nanchang University (9166-27060003-ZD04); the Clinical Research Project of the Second Affiliated Hospital of Nanchang University (2021efyA04); and the Research Foundation of the Education Department of Jiangxi Province (GJJ14085). The authors declare no potential conflicts of interest with respect to the authorship and/or publication of this article.

Funding

This article is funded by Nanchang University, 9166-27060003-ZD04, Li Song, 2021efyA04, Li Song, Education Department of Jiangxi Province, GJJ14085, Fang Dai, and Natural Science Foundation of Jiangxi Province, 20212BBG73006, Li Song.

Author information

Author notes

  1. Fang Dai and Qiangdong Liu contributed equally to this work.

Authors and Affiliations

  1. Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China

    Fang Dai, Qiangdong Liu, **gting Wu, Tian Deng, Hongbiao Zhu & Li Song

  2. School of Public Health, Nanchang University, No.1299, Xuefu Avenue, Nanchang, 330000, Jiangxi, China

    Libin Deng

  3. Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China

    Libin Deng

  4. The Second Clinical Medical School, Nanchang University, Nanchang, China

    Qiangdong Liu & Yuchen Guo

  5. School of Life Sciences, Nanchang University, Nanchang, China

    Ruixiang **e

  6. The Institute of Periodontal Disease, Nanchang University, Nanchang, China

    Fang Dai, Qiangdong Liu, **gting Wu, Tian Deng, Hongbiao Zhu, Libin Deng & Li Song

  7. JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China

    Fang Dai, Qiangdong Liu, **gting Wu, Tian Deng, Hongbiao Zhu, Libin Deng & Li Song

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  1. Fang Dai
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Contributions

FD: study concepts and design; literature research; statistical analysis; manuscript preparation. QL: study concepts and design; statistical analysis; manuscript preparation. YG, RX, JW, TD, HZ: experimental studies/data analysis. LS: guarantor of integrity of the entire study; statistical analysis; experimental studies/data analysis; funding acquisition; manuscript editing. LD: guarantor of integrity of the entire study; statistical analysis; manuscript editing. All the authors gave final approval and agreed to be accountable for all aspects of the work.

Corresponding authors

Correspondence to Libin Deng or Li Song.

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The authors declare that they have no conflict of interest.

Ethical approval

All the procedures were performed in accordance with the ethical standards of the Ethics Committee of the Second Affiliated Hospital of Nanchang University and with the Helsinki Declaration of 1975, as revised in 2008 (5).

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Informed consent was obtained from all the patients for being included in the study.

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Dai, F., Liu, Q., Guo, Y. et al. Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis. Oral Radiol 40, 357–366 (2024). https://doi.org/10.1007/s11282-024-00739-5

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  • DOI: https://doi.org/10.1007/s11282-024-00739-5

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