| Autor: |
Sevda Kurt Bayrakdar, Kaan Orhan, Ibrahim Sevki Bayrakdar, Elif Bilgir, Matvey Ezhov, Maxim Gusarev, Eugene Shumilov |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
BMC Medical Imaging, Vol 21, Iss 1, Pp 1-9 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2342 |
| DOI: |
10.1186/s12880-021-00618-z |
| Popis: |
Abstract Background The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. Results In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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