Among Artificial Intelligence/Machine Learning Methods, Automated Gradient-Boosting Models Accurately Score Intraoral Plaque in Non-Standardized Images

• 主要な著者: Eric Coy (著者), William Santo (著者), Bonnie Jue (著者), Helen Betts (著者), Francisco Ramos-Gomez (著者), Stuart A. Gansky (著者)
• フォーマット: 図書
• 出版事項: Taylor & Francis Group, 2024-12-01T00:00:00Z.
• 主題: article
• オンライン･アクセス: Connect to this object online.

Background Previous automated models inaccurately scored non-standardized plaque images. The objectives were to develop and test automated image selection and intraoral plaque-scoring (primary outcome measure in a prevention trial for preschoolers).Methods Evaluating 1650 plaque-disclosed primary te...