Machine learning-based decision support system for orthognathic diagnosis and treatment planning
Abstract Background Dento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision suppo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-024-04063-6 |
| _version_ | 1797272985168510976 |
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| author | Wen Du Wenjun Bi Yao Liu Zhaokun Zhu Yue Tai En Luo |
| author_facet | Wen Du Wenjun Bi Yao Liu Zhaokun Zhu Yue Tai En Luo |
| author_sort | Wen Du |
| collection | DOAJ |
| description | Abstract Background Dento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support system for treatment of dento-maxillofacial malformations. Methods Patients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes. Results The binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human–computer interaction. There was no statistically significant difference between the actual and AI- groups. Conclusions Machine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers. |
| first_indexed | 2024-03-07T14:38:04Z |
| format | Article |
| id | doaj.art-b6df5e3b2d7140e9982c58b138931fe7 |
| institution | Directory Open Access Journal |
| issn | 1472-6831 |
| language | English |
| last_indexed | 2024-03-07T14:38:04Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj.art-b6df5e3b2d7140e9982c58b138931fe72024-03-05T20:33:39ZengBMCBMC Oral Health1472-68312024-02-0124111310.1186/s12903-024-04063-6Machine learning-based decision support system for orthognathic diagnosis and treatment planningWen Du0Wenjun Bi1Yao Liu2Zhaokun Zhu3Yue Tai4En Luo5State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan UniversitySchool of Electric Power Engineering, Nanjing Institute of TechnologyState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan UniversityState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan UniversityState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan UniversityState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan UniversityAbstract Background Dento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support system for treatment of dento-maxillofacial malformations. Methods Patients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes. Results The binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human–computer interaction. There was no statistically significant difference between the actual and AI- groups. Conclusions Machine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers.https://doi.org/10.1186/s12903-024-04063-6Artificial bee colonyArtificial intelligenceDento-maxillofacial deformitiesDiagnosisMachine learningSurgical plan |
| spellingShingle | Wen Du Wenjun Bi Yao Liu Zhaokun Zhu Yue Tai En Luo Machine learning-based decision support system for orthognathic diagnosis and treatment planning BMC Oral Health Artificial bee colony Artificial intelligence Dento-maxillofacial deformities Diagnosis Machine learning Surgical plan |
| title | Machine learning-based decision support system for orthognathic diagnosis and treatment planning |
| title_full | Machine learning-based decision support system for orthognathic diagnosis and treatment planning |
| title_fullStr | Machine learning-based decision support system for orthognathic diagnosis and treatment planning |
| title_full_unstemmed | Machine learning-based decision support system for orthognathic diagnosis and treatment planning |
| title_short | Machine learning-based decision support system for orthognathic diagnosis and treatment planning |
| title_sort | machine learning based decision support system for orthognathic diagnosis and treatment planning |
| topic | Artificial bee colony Artificial intelligence Dento-maxillofacial deformities Diagnosis Machine learning Surgical plan |
| url | https://doi.org/10.1186/s12903-024-04063-6 |
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