パノラミックX線写真によるディープラーニングベースの自動診断チャート作成:YOLOv11とYOLOv12の比較
Onur Mutlu1, Elif Aslan2, Ali Mert3
1Faculty of Science, Department of Computer Science, Karadeniz Technical University, Trabzon, Türkiye. onurmutlu@ktu.edu.tr.
Odontology
|February 16, 2026
まとめ
YOLOv11は,パノラマX線写真で13の歯科疾患を検出するYOLOv12を上回っています. このディープラーニングモデルは,強力な汎用性を示し,歯科診断と臨床効率を改善するための信頼できるツールを提供します.
科学分野:
- 歯科における人工知能
- 医学イメージングのためのディープラーニング
- 放射線診断のサポート
背景:
- パノラミックX線写真の自動診断チャート作成は,ワークフローの最適化と診断の変動性の軽減に不可欠です.
- 次世代のディープラーニングアーキテクチャは,歯の状態の自動検知を強化する可能性を秘めています.
研究 の 目的:
- 13の歯科疾患を検出するためのYOLOv11とYOLOv12のディープラーニングアーキテクチャのパフォーマンスを比較的に分析する.
- 内部および外部データセットにおけるこれらのモデルの一般化能力を評価する.
主な方法:
- 2,297枚のパノラマX線写真のハイブリッドデータセットが利用されました.
- 模型は,下痢,インプラント,骨の喪失,インパクトを含む状態を検出するために訓練されました.
- 性能はmAP@0.5,精度,リコール,F1スコアを使用して評価されました.
主要な成果:
- YOLOv11は,内部テストセットでは0.857,外部テストセットでは0.806の優れたmAP@0.5を達成しました.
- YOLOv11は,YOLOv12よりもより堅実なパフォーマンスと汎用性を実証しました.
- 検出精度は,微妙な病理 (骨の喪失,下痢) よりも,明確な物体 (冠,インプラント) について高かった.
結論:
- YOLOv11は,YOLOv12と比較して,多クラス歯科オブジェクト検出のためのより信頼性の高いアーキテクチャです.
- YOLOv11モデルは,診断の正確性と効率性を高めるための臨床的意思決定支援ツールとして大きな可能性を秘めている.
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