Application of entire dental panorama image data in artificial intelligence model for age estimation
Abstract Background Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | BMC Oral Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12903-023-03745-x |
| _version_ | 1827581347927949312 |
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| author | Se Hoon Kahm Ji-Youn Kim Seok Yoo Soo-Mi Bae Ji-Eun Kang Sang Hwa Lee |
| author_facet | Se Hoon Kahm Ji-Youn Kim Seok Yoo Soo-Mi Bae Ji-Eun Kang Sang Hwa Lee |
| author_sort | Se Hoon Kahm |
| collection | DOAJ |
| description | Abstract Background Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks. This study evaluated the efficiency of an AI model by applying the entire panoramic image for age estimation. The outcome performances were analyzed through supervised learning (SL) models. Methods Total of 27,877 dental panorama images from 5 to 90 years of age were classified by 2 types of grouping. In type 1 they were classified by each age and in type 2, applying heuristic grouping, the age over 20 years were classified by every 5 years. Wide ResNet (WRN) and DenseNet (DN) were used for supervised learning. In addition, the analysis with ± 3 years of deviation in both types were performed. Results For the DN model, while the type 1 grouping achieved an accuracy of 0.1016 and F1 score of 0.058, the type 2 achieved an accuracy of 0.3146 and F1 score of 0.2027. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.281, 0.7323 respectively; and the F1 score were 0.1768, 0.6583 respectively. For the WRN model, while the type 1 grouping achieved an accuracy of 0.1041 and F1 score of 0.0599, the type 2 achieved an accuracy of 0.3182 and F1 score of 0.2071. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.2716, 0.7323 respectively; and the F1 score were 0.1709, 0.6437 respectively. Conclusions The application of entire panorama image data for supervised with classification by heuristics grouping with ± 3years of deviation for supervised learning models and demonstrated satisfactory outcome for the age estimation. |
| first_indexed | 2024-03-08T22:34:07Z |
| format | Article |
| id | doaj.art-2dee44698b67445292efa74201aad385 |
| institution | Directory Open Access Journal |
| issn | 1472-6831 |
| language | English |
| last_indexed | 2024-03-08T22:34:07Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj.art-2dee44698b67445292efa74201aad3852023-12-17T12:32:04ZengBMCBMC Oral Health1472-68312023-12-012311810.1186/s12903-023-03745-xApplication of entire dental panorama image data in artificial intelligence model for age estimationSe Hoon Kahm0Ji-Youn Kim1Seok Yoo2Soo-Mi Bae3Ji-Eun Kang4Sang Hwa Lee5Department of Dentistry, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDivision of Oral & Maxillofacial Surgery, Department of Dentistry, St. Vincent’s Hospital, College of Medicine, The Catholic University of KoreaUnidocs IncDepartment of Artificial Intelligence, Graduate School, Korea UniversityJINHAKapply CorpDepartment of Dentistry, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaAbstract Background Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks. This study evaluated the efficiency of an AI model by applying the entire panoramic image for age estimation. The outcome performances were analyzed through supervised learning (SL) models. Methods Total of 27,877 dental panorama images from 5 to 90 years of age were classified by 2 types of grouping. In type 1 they were classified by each age and in type 2, applying heuristic grouping, the age over 20 years were classified by every 5 years. Wide ResNet (WRN) and DenseNet (DN) were used for supervised learning. In addition, the analysis with ± 3 years of deviation in both types were performed. Results For the DN model, while the type 1 grouping achieved an accuracy of 0.1016 and F1 score of 0.058, the type 2 achieved an accuracy of 0.3146 and F1 score of 0.2027. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.281, 0.7323 respectively; and the F1 score were 0.1768, 0.6583 respectively. For the WRN model, while the type 1 grouping achieved an accuracy of 0.1041 and F1 score of 0.0599, the type 2 achieved an accuracy of 0.3182 and F1 score of 0.2071. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.2716, 0.7323 respectively; and the F1 score were 0.1709, 0.6437 respectively. Conclusions The application of entire panorama image data for supervised with classification by heuristics grouping with ± 3years of deviation for supervised learning models and demonstrated satisfactory outcome for the age estimation.https://doi.org/10.1186/s12903-023-03745-xAge determinationArtificial intelligenceForensic dentistryPanoramic radiographyDeep learning |
| spellingShingle | Se Hoon Kahm Ji-Youn Kim Seok Yoo Soo-Mi Bae Ji-Eun Kang Sang Hwa Lee Application of entire dental panorama image data in artificial intelligence model for age estimation BMC Oral Health Age determination Artificial intelligence Forensic dentistry Panoramic radiography Deep learning |
| title | Application of entire dental panorama image data in artificial intelligence model for age estimation |
| title_full | Application of entire dental panorama image data in artificial intelligence model for age estimation |
| title_fullStr | Application of entire dental panorama image data in artificial intelligence model for age estimation |
| title_full_unstemmed | Application of entire dental panorama image data in artificial intelligence model for age estimation |
| title_short | Application of entire dental panorama image data in artificial intelligence model for age estimation |
| title_sort | application of entire dental panorama image data in artificial intelligence model for age estimation |
| topic | Age determination Artificial intelligence Forensic dentistry Panoramic radiography Deep learning |
| url | https://doi.org/10.1186/s12903-023-03745-x |
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