Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation
Abstract The current multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CN...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-28442-1 |
| _version_ | 1811175894488711168 |
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| author | Taseef Hasan Farook Saif Ahmed Nafij Bin Jamayet Farah Rashid Aparna Barman Preena Sidhu Pravinkumar Patil Awsaf Mahmood Lisan Sumaya Zabin Eusufzai James Dudley Umer Daood |
| author_facet | Taseef Hasan Farook Saif Ahmed Nafij Bin Jamayet Farah Rashid Aparna Barman Preena Sidhu Pravinkumar Patil Awsaf Mahmood Lisan Sumaya Zabin Eusufzai James Dudley Umer Daood |
| author_sort | Taseef Hasan Farook |
| collection | DOAJ |
| description | Abstract The current multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CNN was first evaluated (phase 1). There were no significant differences in surface area [t-stat(df) = − 0.01 (10), mean difference = − 0.058, P > 0.99] and volume [t-stat(df) = 0.357(10)]. However, the intraoral scans were chosen for phase 2 as they produced a greater level of volumetric details (343.83 ± 43.52 mm3) compared to desktop laser scanning (322.70 ± 40.15 mm3). In phase 2, 120 tooth preparations were digitally synthesized from intraoral scans, and two clinicians designed the respective PDCs using computer-aided design (CAD) workflows on a personal computer setup. Statistical comparison by 3-factor ANOVA demonstrated significant differences in surface area (P < 0.001), volume (P < 0.001), and spatial overlap (P < 0.001), and therefore only the most accurate PDCs (n = 30) were picked to train the neural network (Phase 3). The current 3D-CNN produced a validation accuracy of 60%, validation loss of 0.68–0.87, sensitivity of 1.00, precision of 0.50–0.83, and serves as a proof-of-concept that 3D-CNN can predict and generate PDC prostheses in CAD for restorative dentistry. |
| first_indexed | 2024-04-10T19:43:22Z |
| format | Article |
| id | doaj.art-aff50325f2e0408b9eab2f8fbc5bbd4c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T19:43:22Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-aff50325f2e0408b9eab2f8fbc5bbd4c2023-01-29T12:10:56ZengNature PortfolioScientific Reports2045-23222023-01-011311810.1038/s41598-023-28442-1Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validationTaseef Hasan Farook0Saif Ahmed1Nafij Bin Jamayet2Farah Rashid3Aparna Barman4Preena Sidhu5Pravinkumar Patil6Awsaf Mahmood Lisan7Sumaya Zabin Eusufzai8James Dudley9Umer Daood10Adelaide Dental School, The University of AdelaideDepartment of Electrical and Computer Engineering, North South UniversityRestorative Dentistry Division, School of Dentistry, International Medical University Kuala LumpurSchool of Dental Sciences, Universiti Sains MalaysiaSchool of Dental Sciences, Universiti Sains MalaysiaRestorative Dentistry Division, School of Dentistry, International Medical University Kuala LumpurRestorative Dentistry Division, School of Dentistry, International Medical University Kuala LumpurDepartment of Electrical and Computer Engineering, North South UniversitySchool of Dental Sciences, Universiti Sains MalaysiaAdelaide Dental School, The University of AdelaideRestorative Dentistry Division, School of Dentistry, International Medical University Kuala LumpurAbstract The current multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CNN was first evaluated (phase 1). There were no significant differences in surface area [t-stat(df) = − 0.01 (10), mean difference = − 0.058, P > 0.99] and volume [t-stat(df) = 0.357(10)]. However, the intraoral scans were chosen for phase 2 as they produced a greater level of volumetric details (343.83 ± 43.52 mm3) compared to desktop laser scanning (322.70 ± 40.15 mm3). In phase 2, 120 tooth preparations were digitally synthesized from intraoral scans, and two clinicians designed the respective PDCs using computer-aided design (CAD) workflows on a personal computer setup. Statistical comparison by 3-factor ANOVA demonstrated significant differences in surface area (P < 0.001), volume (P < 0.001), and spatial overlap (P < 0.001), and therefore only the most accurate PDCs (n = 30) were picked to train the neural network (Phase 3). The current 3D-CNN produced a validation accuracy of 60%, validation loss of 0.68–0.87, sensitivity of 1.00, precision of 0.50–0.83, and serves as a proof-of-concept that 3D-CNN can predict and generate PDC prostheses in CAD for restorative dentistry.https://doi.org/10.1038/s41598-023-28442-1 |
| spellingShingle | Taseef Hasan Farook Saif Ahmed Nafij Bin Jamayet Farah Rashid Aparna Barman Preena Sidhu Pravinkumar Patil Awsaf Mahmood Lisan Sumaya Zabin Eusufzai James Dudley Umer Daood Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation Scientific Reports |
| title | Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation |
| title_full | Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation |
| title_fullStr | Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation |
| title_full_unstemmed | Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation |
| title_short | Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation |
| title_sort | computer aided design and 3 dimensional artificial convolutional neural network for digital partial dental crown synthesis and validation |
| url | https://doi.org/10.1038/s41598-023-28442-1 |
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