A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth
Abstract Background Assessing the time required for tooth extraction is the most important factor to consider before surgeries. The purpose of this study was to create a practical predictive model for assessing the time to extract the mandibular third molar tooth using deep learning. The accuracy of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Oral Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12903-022-02614-3 |
| _version_ | 1828121747659948032 |
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| author | Dohyun Kwon Jaemyung Ahn Chang-Soo Kim Dong ohk Kang Jun-Young Paeng |
| author_facet | Dohyun Kwon Jaemyung Ahn Chang-Soo Kim Dong ohk Kang Jun-Young Paeng |
| author_sort | Dohyun Kwon |
| collection | DOAJ |
| description | Abstract Background Assessing the time required for tooth extraction is the most important factor to consider before surgeries. The purpose of this study was to create a practical predictive model for assessing the time to extract the mandibular third molar tooth using deep learning. The accuracy of the model was evaluated by comparing the extraction time predicted by deep learning with the actual time required for extraction. Methods A total of 724 panoramic X-ray images and clinical data were used for artificial intelligence (AI) prediction of extraction time. Clinical data such as age, sex, maximum mouth opening, body weight, height, the time from the start of incision to the start of suture, and surgeon’s experience were recorded. Data augmentation and weight balancing were used to improve learning abilities of AI models. Extraction time predicted by the concatenated AI model was compared with the actual extraction time. Results The final combined model (CNN + MLP) model achieved an R value of 0.8315, an R-squared value of 0.6839, a p-value of less than 0.0001, and a mean absolute error (MAE) of 2.95 min with the test dataset. Conclusions Our proposed model for predicting time to extract the mandibular third molar tooth performs well with a high accuracy in clinical practice. |
| first_indexed | 2024-04-11T14:23:44Z |
| format | Article |
| id | doaj.art-d30c82fb27234a7eb6d22fc01406ed92 |
| institution | Directory Open Access Journal |
| issn | 1472-6831 |
| language | English |
| last_indexed | 2024-04-11T14:23:44Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj.art-d30c82fb27234a7eb6d22fc01406ed922022-12-22T04:18:56ZengBMCBMC Oral Health1472-68312022-12-012211810.1186/s12903-022-02614-3A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar toothDohyun Kwon0Jaemyung Ahn1Chang-Soo Kim2Dong ohk Kang3Jun-Young Paeng4Department of Oral and Maxillofacial Surgery, Samsung Medical Center, Sungkyunkwan University School of MedicineDepartment of Oral and Maxillofacial Surgery, Samsung Medical Center, Sungkyunkwan University School of MedicineDepartment of Oral and Maxillofacial Surgery, Samsung Medical Center, Sungkyunkwan University School of MedicineDepartment of Oral and Maxillofacial Surgery, Samsung Medical Center, Sungkyunkwan University School of MedicineDepartment of Oral and Maxillofacial Surgery, Samsung Medical Center, Sungkyunkwan University School of MedicineAbstract Background Assessing the time required for tooth extraction is the most important factor to consider before surgeries. The purpose of this study was to create a practical predictive model for assessing the time to extract the mandibular third molar tooth using deep learning. The accuracy of the model was evaluated by comparing the extraction time predicted by deep learning with the actual time required for extraction. Methods A total of 724 panoramic X-ray images and clinical data were used for artificial intelligence (AI) prediction of extraction time. Clinical data such as age, sex, maximum mouth opening, body weight, height, the time from the start of incision to the start of suture, and surgeon’s experience were recorded. Data augmentation and weight balancing were used to improve learning abilities of AI models. Extraction time predicted by the concatenated AI model was compared with the actual extraction time. Results The final combined model (CNN + MLP) model achieved an R value of 0.8315, an R-squared value of 0.6839, a p-value of less than 0.0001, and a mean absolute error (MAE) of 2.95 min with the test dataset. Conclusions Our proposed model for predicting time to extract the mandibular third molar tooth performs well with a high accuracy in clinical practice.https://doi.org/10.1186/s12903-022-02614-3Mandibular third molarExtraction timePredictive modelConcatenation approachArtificial intelligence |
| spellingShingle | Dohyun Kwon Jaemyung Ahn Chang-Soo Kim Dong ohk Kang Jun-Young Paeng A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth BMC Oral Health Mandibular third molar Extraction time Predictive model Concatenation approach Artificial intelligence |
| title | A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth |
| title_full | A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth |
| title_fullStr | A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth |
| title_full_unstemmed | A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth |
| title_short | A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth |
| title_sort | deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth |
| topic | Mandibular third molar Extraction time Predictive model Concatenation approach Artificial intelligence |
| url | https://doi.org/10.1186/s12903-022-02614-3 |
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