Deep learning for the identification of ridge deficiency around dental implants
© 2023 Wiley Periodicals LLC..
OBJECTIVES: This study aimed to use a deep learning (DL) approach for the automatic identification of the ridge deficiency around dental implants based on an image slice from cone-beam computerized tomography (CBCT).
MATERIALS AND METHODS: Single slices crossing the central long-axis of 630 mandibular and 845 maxillary virtually placed implants (4-5 mm diameter, 10 mm length) in 412 patients were used. The ridges were classified based on the intraoral bone-implant support and sinus floor location. The slices were either preprocessed by alveolar ridge homogenizing prior to DL (preprocessed) or left unpreprocessed. A convolutional neural network with ResNet-50 architecture was employed for DL.
RESULTS: The model achieved an accuracy of >98.5% on the unpreprocessed image slices and was found to be superior to the accuracy observed on the preprocessed slices. On the mandible, model accuracy was 98.91 ± 1.45%, and F1 score, a measure of a model's accuracy in binary classification tasks, was lowest (97.30%) on the ridge with a combined horizontal-vertical defect. On the maxilla, model accuracy was 98.82 ± 1.11%, and the ridge presenting an implant collar-sinus floor distance of 5-10 mm with a dehiscence defect had the lowest F1 score (95.86%). To achieve >90% model accuracy, ≥441 mandibular slices or ≥592 maxillary slices were required.
CONCLUSIONS: The ridge deficiency around dental implants can be identified using DL from CBCT image slices without the need for preprocessed homogenization. The model will be further strengthened by implementing more clinical expertise in dental implant treatment planning and incorporating multiple slices to classify 3-dimensional implant-ridge relationships.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:26 |
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| Enthalten in: |
Clinical implant dentistry and related research - 26(2024), 2 vom: 28. Apr., Seite 376-384 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lin, Cheng-Hung [VerfasserIn] |
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| Links: |
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| Themen: |
Alveolar process |
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| Anmerkungen: |
Date Completed 09.04.2024 Date Revised 09.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/cid.13301 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM366425471 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 Wiley Periodicals LLC. | ||
| 520 | |a OBJECTIVES: This study aimed to use a deep learning (DL) approach for the automatic identification of the ridge deficiency around dental implants based on an image slice from cone-beam computerized tomography (CBCT) | ||
| 520 | |a MATERIALS AND METHODS: Single slices crossing the central long-axis of 630 mandibular and 845 maxillary virtually placed implants (4-5 mm diameter, 10 mm length) in 412 patients were used. The ridges were classified based on the intraoral bone-implant support and sinus floor location. The slices were either preprocessed by alveolar ridge homogenizing prior to DL (preprocessed) or left unpreprocessed. A convolutional neural network with ResNet-50 architecture was employed for DL | ||
| 520 | |a RESULTS: The model achieved an accuracy of >98.5% on the unpreprocessed image slices and was found to be superior to the accuracy observed on the preprocessed slices. On the mandible, model accuracy was 98.91 ± 1.45%, and F1 score, a measure of a model's accuracy in binary classification tasks, was lowest (97.30%) on the ridge with a combined horizontal-vertical defect. On the maxilla, model accuracy was 98.82 ± 1.11%, and the ridge presenting an implant collar-sinus floor distance of 5-10 mm with a dehiscence defect had the lowest F1 score (95.86%). To achieve >90% model accuracy, ≥441 mandibular slices or ≥592 maxillary slices were required | ||
| 520 | |a CONCLUSIONS: The ridge deficiency around dental implants can be identified using DL from CBCT image slices without the need for preprocessed homogenization. The model will be further strengthened by implementing more clinical expertise in dental implant treatment planning and incorporating multiple slices to classify 3-dimensional implant-ridge relationships | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a alveolar process | |
| 650 | 4 | |a alveolar ridge augmentation | |
| 650 | 4 | |a artificial intelligence | |
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| 700 | 1 | |a Chang, Hao-Chieh |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Chin-Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Po-Chun |e verfasserin |4 aut | |
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