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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張博鈞 | zh_TW |
| dc.contributor.advisor | Po-Chun Chang | en |
| dc.contributor.author | 尤俐文 | zh_TW |
| dc.contributor.author | Li-Wen Yu | en |
| dc.date.accessioned | 2024-08-21T16:41:26Z | - |
| dc.date.available | 2024-08-22 | - |
| dc.date.copyright | 2024-08-21 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-06 | - |
| dc.identifier.citation | [1] Binon PP. Implants and components: entering the new millennium. Int. J. Oral Maxillofac. Implants. 2000;15:76–94.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94928 | - |
| dc.description.abstract | 研究目標
本研究目標為建立一個透過錐狀射束電腦斷層掃描cone-beam computerized tomography (CBCT) 影像自動判讀植牙骨缺損型態分類的深度學習模型，以提供植牙治療計畫建議。 研究方法 使用177名患者（83名男性和 94名女性，年齡 56.49 ± 12.76 歲）的 630 個下顎CBCT影像切片，虛擬植入植體（直徑 4-5 毫米，長度 10 毫米），切片皆根據虛擬植體的長軸定向並透過人工標記。齒槽嵴缺損型態分成五類，採用ResNet-50卷積神經網路模型進行訓練。 研究結果 該模型在未經預處理影像切片的準確度為98.91 ± 1.45%，優於經預處理影像的準確度92.85 ± 2.60%。以各缺損型態分類的模型性能而言，未經預處理的影像切片之準確率和 F1 分數分別為 99.05%–99.84% 和 97.30%–99.66%，經預處理的切片之準確度和 F1 分數分別為 96.03%–98.89% 和 87.92%–96.71%。 結論 透過深度學習判讀CBCT影像，無需經過預處理即可高準確率識別出植牙骨缺損型態分類。 | zh_TW |
| dc.description.abstract | Objectives
The aim of this study was to establish a deep learning model for the automatic identification of the ridge deficiency around dental implants based on an image slice from cone-beam computerized tomography (CBCT) in order to provide recommendations for dental implant treatment plan. Materials and Methods A total of 630 CBCT image slices of the mandible from respective implants in 117 patients (83 males and 94 females, age 56.49 ± 12.76 years) were used as the datasets in this study. Virtual implant fixtures (4-5 mm diameter, 10mm length) were placed in the implant treatment planning software. In each implant, one slice crossing the central long-axis implant was chosen and the classifications of edentulous ridges were labeled. Alveolar ridge defect types were divided into five classifications. A convolutional neural network with ResNet-50 architecture was employed for deep learning. Results The model achieved an accuracy of 98.91 ± 1.45% on the unpreprocessed image slices and was found to be superior to the accuracy of 92.85 ± 2.60% observed on the preprocessed slices. In terms of model performance for each defect type classification, the accuracy and F1 scores for unpreprocessed image slices were 99.05%–99.84% and 97.30%–99.66%, respectively, and 96.03%–98.89% and 87.92%–96.71%, respectively, for preprocessed slices. Conclusion By identifying CBCT images through deep learning, the ridge deficiency around dental implants could be correctly classified with high accuracy without preprocessing. | en |
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| dc.description.provenance | Made available in DSpace on 2024-08-21T16:41:26Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 摘要 III ABSTRACT IV 目次 V 圖次 VII 表次 VIII 第1章 緒論 1 1.1 拔牙後齒槽嵴型態的變化 2 1.2 缺牙齒槽嵴之臨床分類 4 1.3 齒槽嵴臨床重建準則 6 1.4 數位化植牙工作流程之現況 10 1.5 人工智慧在植牙治療之角色 11 第2章 研究目標 15 2.1 研究假說 15 2.2 研究目標 15 第3章 研究方法 16 3.1 倫理及影像資料搜集 16 3.2 虛擬植體植入 17 3.3 植牙齒槽嵴缺損分類與標記 18 3.4 信度分析（Assessments for the Reliability） 19 3.5 深度學習的卷積神經網絡（Convolutional neural network for DL） 20 3.6 熱區圖視覺化 (Heatmap visualization) 21 3.7 數據呈現和模型性能(Data presentation and model performance) 22 第4章 研究結果 23 4.1 資料搜集與標記 23 4.2 模型預測性能 24 4.3 模型分類可視覺化( Visualization of model classification ) 25 4.4 資料數量與模型準確性之關係 26 第5章 討論與建議 27 5.1 討論 27 5.2 未來研究方向 29 第6章 結論 31 參考文獻 32 附錄 圖與表 46 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 影像辨識 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 錐狀射束電腦斷層掃描 | zh_TW |
| dc.subject | 齒槽嵴缺損 | zh_TW |
| dc.subject | 人工植牙 | zh_TW |
| dc.subject | deep learning | en |
| dc.subject | ridge deficency | en |
| dc.subject | image recognition | en |
| dc.subject | CBCT | en |
| dc.subject | dental implant | en |
| dc.title | 人工智慧應用於錐狀射束電腦斷層掃描進行牙科植體治療規劃之研究 | zh_TW |
| dc.title | The Application of Artificial Intelligence for Dental Implant Treatment Planning via Cone Beam Computed Tomography | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 王振穎;張維仁 | zh_TW |
| dc.contributor.oralexamcommittee | Chen-Ying Wang;Wei-Jen Chang | en |
| dc.subject.keyword | 齒槽嵴缺損,深度學習,人工植牙,錐狀射束電腦斷層掃描,影像辨識, | zh_TW |
| dc.subject.keyword | ridge deficency,deep learning,dental implant,CBCT,image recognition, | en |
| dc.relation.page | 57 | - |
| dc.identifier.doi | 10.6342/NTU202403694 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-07 | - |
| dc.contributor.author-college | 醫學院 | - |
| dc.contributor.author-dept | 臨床牙醫學研究所 | - |
| 顯示於系所單位： | 臨床牙醫學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf | 6.79 MB | Adobe PDF | 檢視/開啟 |
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