DentalArch: AI-Based Arch Shape Detection in Orthodontics
Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, we...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2567 |
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| author | J. D. Tamayo-Quintero J. B. Gómez-Mendoza S. V. Guevara-Pérez |
| author_facet | J. D. Tamayo-Quintero J. B. Gómez-Mendoza S. V. Guevara-Pérez |
| author_sort | J. D. Tamayo-Quintero |
| collection | DOAJ |
| description | Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, we refined our dataset to 50 models, ensuring a focused and detailed analysis. Plaster casts were digitized into 3D models with AutoScan-DS-EX. Three trained evaluators then measured mesiodistal and arch widths using MeshLab. The development of DentalArch was undertaken in two versions: the first version incorporates 18 input parameters, including mesiodistal widths (from the first molar to the first molar, totaling 14) and arch widths (1 intercanine, 2 interpremolar, and 1 intermolar, totaling 4); the second version uses only 4 parameters related to arch widths. Both versions aim to predict the arch shape. An evaluation of 28 machine learning methods through a k = 5-fold cross-validation was conducted to determine the most effective techniques. Results: In the tests, the performance evaluation of the DentalArch software in detecting arch shapes revealed that version 1, which analyzes 18 parameters, achieved an accuracy of 94.7% for the lower arch and 93% for the upper arch. The more streamlined version 2, which assesses only four parameters, also showed high precision with an accuracy of 93.0% for the lower arch and 92.7% for the upper arch. Conclusions: DentalArch provides a tool with potential use in orthodontic diagnostics, particularly in the task of arch shape classification. The software offers a less subjective and data-driven approach to arch shape determination. Moreover, the open-source nature of DentalArch ensures its global availability and encourages contributions from the orthodontic community. |
| first_indexed | 2024-04-24T18:34:17Z |
| format | Article |
| id | doaj.art-4e565742c5f048ef974ef8d4cf168567 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:34:17Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4e565742c5f048ef974ef8d4cf1685672024-03-27T13:20:07ZengMDPI AGApplied Sciences2076-34172024-03-01146256710.3390/app14062567DentalArch: AI-Based Arch Shape Detection in OrthodonticsJ. D. Tamayo-Quintero0J. B. Gómez-Mendoza1S. V. Guevara-Pérez2Department of Electric, Electronic and Computing Engineering, Universidad Nacional de Colombia, Sede Manizales, Manizales 170003, ColombiaDepartment of Electric, Electronic and Computing Engineering, Universidad Nacional de Colombia, Sede Manizales, Manizales 170003, ColombiaDepartment of Oral Health-Orthodontics, Faculty of Dentistry, Universidad Nacional de Colombia, Sede Bogota, Bogota 050034, ColombiaObjective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, we refined our dataset to 50 models, ensuring a focused and detailed analysis. Plaster casts were digitized into 3D models with AutoScan-DS-EX. Three trained evaluators then measured mesiodistal and arch widths using MeshLab. The development of DentalArch was undertaken in two versions: the first version incorporates 18 input parameters, including mesiodistal widths (from the first molar to the first molar, totaling 14) and arch widths (1 intercanine, 2 interpremolar, and 1 intermolar, totaling 4); the second version uses only 4 parameters related to arch widths. Both versions aim to predict the arch shape. An evaluation of 28 machine learning methods through a k = 5-fold cross-validation was conducted to determine the most effective techniques. Results: In the tests, the performance evaluation of the DentalArch software in detecting arch shapes revealed that version 1, which analyzes 18 parameters, achieved an accuracy of 94.7% for the lower arch and 93% for the upper arch. The more streamlined version 2, which assesses only four parameters, also showed high precision with an accuracy of 93.0% for the lower arch and 92.7% for the upper arch. Conclusions: DentalArch provides a tool with potential use in orthodontic diagnostics, particularly in the task of arch shape classification. The software offers a less subjective and data-driven approach to arch shape determination. Moreover, the open-source nature of DentalArch ensures its global availability and encourages contributions from the orthodontic community.https://www.mdpi.com/2076-3417/14/6/2567arch dental formorthodonticsartificial intelligenceDentalArch softwareAutoScan-DS-EXmachine learning |
| spellingShingle | J. D. Tamayo-Quintero J. B. Gómez-Mendoza S. V. Guevara-Pérez DentalArch: AI-Based Arch Shape Detection in Orthodontics Applied Sciences arch dental form orthodontics artificial intelligence DentalArch software AutoScan-DS-EX machine learning |
| title | DentalArch: AI-Based Arch Shape Detection in Orthodontics |
| title_full | DentalArch: AI-Based Arch Shape Detection in Orthodontics |
| title_fullStr | DentalArch: AI-Based Arch Shape Detection in Orthodontics |
| title_full_unstemmed | DentalArch: AI-Based Arch Shape Detection in Orthodontics |
| title_short | DentalArch: AI-Based Arch Shape Detection in Orthodontics |
| title_sort | dentalarch ai based arch shape detection in orthodontics |
| topic | arch dental form orthodontics artificial intelligence DentalArch software AutoScan-DS-EX machine learning |
| url | https://www.mdpi.com/2076-3417/14/6/2567 |
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