Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance

Abstract Background Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determinin...

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Main Authors:	Paniti Achararit, Chawan Manaspon, Chavin Jongwannasiri, Promphakkon Kulthanaamondhita, Chumpot Itthichaisri, Soranun Chantarangsu, Thanaphum Osathanon, Ekarat Phattarataratip, Kraisorn Sappayatosok
Format:	Article
Language:	English
Published:	BMC 2025-01-01
Series:	BMC Oral Health
Subjects:	Impacted tooth Artificial intelligence Deep learning Convolutional neural network Pederson difficulty index
Online Access:	https://doi.org/10.1186/s12903-025-05425-4
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Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance

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