3D tooth identification for forensic dentistry using deep learning
Abstract The classification of intraoral teeth structures is a critical component in modern dental analysis and forensic dentistry. Traditional methods, relying on 2D imaging, often suffer from limitations in accuracy and comprehensiveness due to the complex three-dimensional (3D) nature of dental a...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-06017-y |
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| author | Hamza Mouncif Amine Kassimi Thierry Bertin Gardelle Hamid Tairi Jamal Riffi |
| author_facet | Hamza Mouncif Amine Kassimi Thierry Bertin Gardelle Hamid Tairi Jamal Riffi |
| author_sort | Hamza Mouncif |
| collection | DOAJ |
| description | Abstract The classification of intraoral teeth structures is a critical component in modern dental analysis and forensic dentistry. Traditional methods, relying on 2D imaging, often suffer from limitations in accuracy and comprehensiveness due to the complex three-dimensional (3D) nature of dental anatomy. Although 3D imaging introduces the third dimension, offering a more comprehensive view, it also introduces additional challenges due to the irregular nature of the data. Our proposed approach addresses these issues with a novel method that extracts critical representative features from 3D tooth models and transforms them into a 2D image format suitable for detailed analysis. The 2D images are subsequently processed using a recurrent neural network (RNN) architecture, which effectively detects complex patterns essential for accurate classification, while its capability to manage sequential data is further augmented by fully connected layers specifically designed for this purpose. This innovative approach improves accuracy and diagnostic efficiency by reducing manual analysis and speeding up processing time, overcoming the challenges of 3D data irregularity and leveraging its detailed representation, thereby setting a new standard in dental identification. |
| format | Article |
| id | doaj-art-50f9df110705460db416d74a06b26c13 |
| institution | Kabale University |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-50f9df110705460db416d74a06b26c132025-08-20T03:52:24ZengBMCBMC Oral Health1472-68312025-04-0125111110.1186/s12903-025-06017-y3D tooth identification for forensic dentistry using deep learningHamza Mouncif0Amine Kassimi1Thierry Bertin Gardelle2Hamid Tairi3Jamal Riffi4LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah UniversityLISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University3D Smart FactoryLISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah UniversityLISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah UniversityAbstract The classification of intraoral teeth structures is a critical component in modern dental analysis and forensic dentistry. Traditional methods, relying on 2D imaging, often suffer from limitations in accuracy and comprehensiveness due to the complex three-dimensional (3D) nature of dental anatomy. Although 3D imaging introduces the third dimension, offering a more comprehensive view, it also introduces additional challenges due to the irregular nature of the data. Our proposed approach addresses these issues with a novel method that extracts critical representative features from 3D tooth models and transforms them into a 2D image format suitable for detailed analysis. The 2D images are subsequently processed using a recurrent neural network (RNN) architecture, which effectively detects complex patterns essential for accurate classification, while its capability to manage sequential data is further augmented by fully connected layers specifically designed for this purpose. This innovative approach improves accuracy and diagnostic efficiency by reducing manual analysis and speeding up processing time, overcoming the challenges of 3D data irregularity and leveraging its detailed representation, thereby setting a new standard in dental identification.https://doi.org/10.1186/s12903-025-06017-y3D mesh processingTeeth classificationDental identificationForensic dentistry |
| spellingShingle | Hamza Mouncif Amine Kassimi Thierry Bertin Gardelle Hamid Tairi Jamal Riffi 3D tooth identification for forensic dentistry using deep learning BMC Oral Health 3D mesh processing Teeth classification Dental identification Forensic dentistry |
| title | 3D tooth identification for forensic dentistry using deep learning |
| title_full | 3D tooth identification for forensic dentistry using deep learning |
| title_fullStr | 3D tooth identification for forensic dentistry using deep learning |
| title_full_unstemmed | 3D tooth identification for forensic dentistry using deep learning |
| title_short | 3D tooth identification for forensic dentistry using deep learning |
| title_sort | 3d tooth identification for forensic dentistry using deep learning |
| topic | 3D mesh processing Teeth classification Dental identification Forensic dentistry |
| url | https://doi.org/10.1186/s12903-025-06017-y |
| work_keys_str_mv | AT hamzamouncif 3dtoothidentificationforforensicdentistryusingdeeplearning AT aminekassimi 3dtoothidentificationforforensicdentistryusingdeeplearning AT thierrybertingardelle 3dtoothidentificationforforensicdentistryusingdeeplearning AT hamidtairi 3dtoothidentificationforforensicdentistryusingdeeplearning AT jamalriffi 3dtoothidentificationforforensicdentistryusingdeeplearning |