垂直根骨折の診断における人工知能の活用-体系的なレビュー
Abdulmajeed Saeed Alshahrani1, Ahmed Ali Alelyani1, Ahmad Jabali2
1Department of Restorative Dentistry, Division of Endodontics, College of Dentistry, Najran University, Najran 61441, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|February 13, 2026
まとめ
人工知能 (AI) は,歯科イメージングの様式を通じて垂直根骨折 (VRF) を検出する有望なことを示しています. AIと組み合わせたコーンビームコンピューティングトモグラフィー (CBCT) は,より多くの研究が必要ですが,最も高い精度が得られます.
科学分野:
- 歯科 歯科は歯科を専門とするものです.
- 放射線学 放射線学
- 人工知能 (AI) とは,人工知能 (AI) のことです.
背景:
- 垂直根骨折 (VRF) は,微妙なX線写真の兆候のために診断することが困難です.
- 既存のイメージング技術とAIの性能は,各モダリティにわたって十分に確立されていません.
研究 の 目的:
- 人工知能支援のVRF検出をペリアピカルX線写真,パノラマX線写真,CBCTで体系的にレビューし,比較する.
- VRF検出におけるAIの診断性能,方法論的品質,および限界を評価する.
主な方法:
- 2025年1月までの主要データベースの体系的な文献レビュー.
- 対象となった研究は,人工知能を用いてVRFをペリアピカル画像,パノラマ画像,CBCT画像で検出した研究であった.
- データ抽出はAIモデル,データセット,イメージング,検証,診断メトリックに焦点を当てた;バイアスのリスクはQUADAS-2で評価された.
主要な成果:
- 10件の研究では,主にCNNベースのAIモデルが使用されました.
- CBCTベースのAIは,最高精度 (91.4-97.8%) と特異性 (90.7-100%) を達成しました.
- 周周放射線写真モデルは高精度 (最大95.7%) を示したが,パノラマ放射線写真モデルは感度が低いが精度が高い.
結論:
- AI支援のVRF検出は,特にCBCTでは有望である.
- 現在の証拠は,方法論的な異質性と不十分な臨床検証によって制限されています.
- 標準化された方法論と臨床検証によるさらなる研究が必要である.
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