AI Detection Model of Extra Root Canals in Mandibular Premolars Using CBCT Scans
Diagnostic Accuracy of a Deep Learning Model (Artificial Intelligence) for Detecting Extra Root Canals in Mandibular Premolars on CBCT Images: Diagnostic Accuracy Study.
Successful endodontic treatment depends on the complete identification and management of the entire root canal system. Missed root canals are a major cause of endodontic failure, particularly in mandibular premolars, which exhibit considerable anatomical variability and may contain additional root canals that are difficult to detect using conventional diagnostic methods.
Cone Beam Computed Tomography (CBCT) provides three-dimensional visualization of root canal anatomy and has significantly improved the detection of anatomical variations. However, interpretation of CBCT images remains dependent on the experience and expertise of the clinician, leading to potential observer variability and missed diagnoses.
Recent advances in artificial intelligence (AI), particularly deep learning models based on convolutional neural networks, have shown promising results in dental image analysis and diagnostic support. AI-assisted diagnostic systems may improve the accuracy, consistency, and efficiency of CBCT interpretation by automatically identifying complex anatomical structures.
The aim of this retrospective diagnostic accuracy study is to evaluate the performance of a newly developed deep learning model for the detection of extra root canals in mandibular premolars using CBCT images. The diagnostic accuracy of the AI model will be assessed by comparing its findings with the assessments of experienced oral and maxillofacial radiologists, which will serve as the reference standard.
A total of 272 CBCT scans of mandibular premolars from Egyptian patients will be included according to predefined eligibility criteria. Diagnostic performance will be evaluated using measures including sensitivity, specificity, positive predictive value, and negative predictive value.
The findings of this study may provide evidence regarding the clinical applicability of AI-assisted diagnostic tools in endodontics and contribute to improved detection of complex root canal anatomy, reduced incidence of missed canals, and enhanced treatment outcomes.
調査の概要
状態
条件
詳細な説明
The goal of this observational study is to evaluate whether a deep learning artificial intelligence (AI) model can accurately detect extra root canals in mandibular premolars using Cone Beam Computed Tomography (CBCT) images in Egyptian patients. The main questions it aims to answer are:
- Can the AI model accurately detect extra root canals in mandibular premolars on CBCT scans?
- Is the diagnostic accuracy of the AI model comparable to that of experienced oral and maxillofacial radiologists? Researchers will compare the results generated by the AI model with the assessments of experienced radiologists, which will serve as the reference standard.
Participants will:
- Provide previously acquired CBCT scans that meet the study eligibility criteria.
- Have their CBCT images analyzed by the AI model.
- Have their CBCT images independently evaluated by experienced radiologists for comparison with the AI findings.
The study findings may help determine the potential role of AI-assisted diagnostic tools in improving the detection of complex root canal anatomy and supporting endodontic diagnosis
研究の種類
入学 (推定)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前：Ayah Tarek, PHD candidate
- 電話番号：20201221902479
- メール：ayahtarek94@gmail.com
参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
Inclusion Criteria:
- CBCT scans of mandibular molars of Egyptian patients aging from 18 to 65 years old
- Small Field of view (FOV) including maximum a quadrant
- Voxel size not larger than 2mm
- Mandibular premolars showing complete root formation
- Carious or non-carious teeth
- Absence of artifacts.
Exclusion Criteria:
- Mandibular first and second premolars with developmental anomalies, external or internal root resorption, root canal calcification, previous root canal treatment, post restorations, and/or root caries
- CBCT images of sub-optimal quality or artifacts/high scatter interfering with proper assessment
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：診断
- 割り当て：ランダム化
- 介入モデル：並列代入
- マスキング：なし（オープンラベル）
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
|
実験的：Mandibular premolars with single canals
|
It is a study to detect the diagnostic accuracy of AI model to detect extra canals in mandibular premolars
|
|
実験的：Mandibular premolars with more than one canal
|
It is a study to detect the diagnostic accuracy of AI model to detect extra canals in mandibular premolars
|
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Diagnostic Accuracy of the Deep Learning Model for Detection of Extra Root Canals in Mandibular Premolars
時間枠：During the procedure
|
Diagnostic accuracy of the AI model will be determined by comparison with expert radiologist assessment.
|
During the procedure
|
二次結果の測定
結果測定 |
時間枠 |
|---|---|
|
Sensitivity of the AI Model Specificity of the AI Model Positive Predictive Value (PPV) Negative Predictive Value (NPV)
時間枠：During the procedure
|
During the procedure
|
協力者と研究者
スポンサー
出版物と役立つリンク
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
- 7.1.1
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