口腔内画像と質問票を用いたう蝕スクリーニングのためのマルチモーダルモデル
Zhuoying Liu1, Junjie Li1, Siwei Wang1
1Department of Preventive Dentistry, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong, China.
International dental journal
|February 6, 2026
まとめ
新しい深層学習モデルは、歯科画像と質問票を統合して小児のう蝕の早期発見を改善します。このマルチモーダルアプローチは、早期のう窩の再検査を改善し、集団レベルの歯科スクリーニングのための有望なツールを提供します。
科学分野:
- 歯科における人工知能
- 医用画像解析
- 公衆衛生
背景:
- 歯科う蝕は、効果的なスクリーニング方法を必要とする一般的な小児疾患です。
- 現在のスクリーニングは視診に依存することが多く、初期の兆候を見逃す可能性があります。
- 深層学習は、自動化された正確な歯科診断の可能性を提供します。
主な方法:
- 子供たちから7671枚の口腔内画像(咬合面および平滑面)を収集しました。
- 臨床画像のラベリング(う蝕なし、初期、中等度から重度)とともに健康状態に関する質問票を利用しました。
- 画像のみのモデルと比較して、マルチモーダル深層学習モデルをトレーニングおよび検証し、精度、適合率、再現率、F1スコア、ROC-AUCを使用しました。
結論:
- マルチモーダル深層学習モデルは、初期の歯科う蝕の検出において画像のみのモデルよりも優れたパフォーマンスを発揮します。
- このインテリジェントスクリーニングモデルは、集団レベルでのう蝕サーベイランスの可能性を示しています。
- 一般化可能性を確認するために、さらなる大規模な多施設での検証が推奨されます。
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