Bayesian network for predicting mandibular third molar extraction difficulty
Abstract Background This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results base...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-05432-5 |
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| author | Tian Meng Zhiyong Zhang Xiao Zhang Chao Zhang |
| author_facet | Tian Meng Zhiyong Zhang Xiao Zhang Chao Zhang |
| author_sort | Tian Meng |
| collection | DOAJ |
| description | Abstract Background This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient’s personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications. Methods Relevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network. First, the qualitative mechanism knowledge, including the effect of risk factors on the extraction difficulty and the causal relationships between risk factors, was analysed to establish the framework of the Bayesian network. Then, the quantitative knowledge, including the occurrence probability of the parent nodes and the conditional probability table of the nodes with causal relationships, was given by the surgeon experience and calculated using the Dempster-Shafer evidence theory. According to the framework and likelihoods and relationships of risk factors, the Bayesian network model was established. Results This Bayesian network model analysed the weight by sensitivity of each risk factor and expressed the interaction relationship among risk factors as well as the effect of risk factors on extraction difficulty quantitatively. This Bayesian network model showed quantitative analysis results for extraction difficulty and key risk factors. The Bayesian network model revealed that the relationship to the inferior alveolar nerve, surgeon experience and patient anxiety were the most important risk factors for extraction difficulty. By integrating these patient-specific risk factors across the entire surgical process, this model could be used during preoperative planning to identify high-risk cases and to optimize resource allocation; during intraoperative management to tailor surgical techniques; and during postoperative follow-up to establish targeted follow-up protocols for high-risk patients. Moreover, this Bayesian network model can flexibly improve inclusion factors and conditional probabilities with the development of relevant research and expert opinions, as well as change states and probabilities of relevant nodes based on actual clinical conditions. Conclusions A model for predicting the difficulty of mandibular third molar extraction was established based on a Bayesian network. |
| format | Article |
| id | doaj-art-e89d611a684a4a3abecd70905e413737 |
| institution | DOAJ |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-e89d611a684a4a3abecd70905e4137372025-08-20T02:40:36ZengBMCBMC Oral Health1472-68312025-01-012511810.1186/s12903-025-05432-5Bayesian network for predicting mandibular third molar extraction difficultyTian Meng0Zhiyong Zhang1Xiao Zhang2Chao Zhang3First Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental MaterialsFirst Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental MaterialsFirst Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental MaterialsSub-Institute of Public Safety Standardization, China National Institute of StandardizationAbstract Background This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient’s personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications. Methods Relevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network. First, the qualitative mechanism knowledge, including the effect of risk factors on the extraction difficulty and the causal relationships between risk factors, was analysed to establish the framework of the Bayesian network. Then, the quantitative knowledge, including the occurrence probability of the parent nodes and the conditional probability table of the nodes with causal relationships, was given by the surgeon experience and calculated using the Dempster-Shafer evidence theory. According to the framework and likelihoods and relationships of risk factors, the Bayesian network model was established. Results This Bayesian network model analysed the weight by sensitivity of each risk factor and expressed the interaction relationship among risk factors as well as the effect of risk factors on extraction difficulty quantitatively. This Bayesian network model showed quantitative analysis results for extraction difficulty and key risk factors. The Bayesian network model revealed that the relationship to the inferior alveolar nerve, surgeon experience and patient anxiety were the most important risk factors for extraction difficulty. By integrating these patient-specific risk factors across the entire surgical process, this model could be used during preoperative planning to identify high-risk cases and to optimize resource allocation; during intraoperative management to tailor surgical techniques; and during postoperative follow-up to establish targeted follow-up protocols for high-risk patients. Moreover, this Bayesian network model can flexibly improve inclusion factors and conditional probabilities with the development of relevant research and expert opinions, as well as change states and probabilities of relevant nodes based on actual clinical conditions. Conclusions A model for predicting the difficulty of mandibular third molar extraction was established based on a Bayesian network.https://doi.org/10.1186/s12903-025-05432-5Tooth extractionArtificial intelligenceClinical decision-makingRisk assessmentPreoperative periodIntraoperative complications |
| spellingShingle | Tian Meng Zhiyong Zhang Xiao Zhang Chao Zhang Bayesian network for predicting mandibular third molar extraction difficulty BMC Oral Health Tooth extraction Artificial intelligence Clinical decision-making Risk assessment Preoperative period Intraoperative complications |
| title | Bayesian network for predicting mandibular third molar extraction difficulty |
| title_full | Bayesian network for predicting mandibular third molar extraction difficulty |
| title_fullStr | Bayesian network for predicting mandibular third molar extraction difficulty |
| title_full_unstemmed | Bayesian network for predicting mandibular third molar extraction difficulty |
| title_short | Bayesian network for predicting mandibular third molar extraction difficulty |
| title_sort | bayesian network for predicting mandibular third molar extraction difficulty |
| topic | Tooth extraction Artificial intelligence Clinical decision-making Risk assessment Preoperative period Intraoperative complications |
| url | https://doi.org/10.1186/s12903-025-05432-5 |
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