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Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection
3
Zitationen
7
Autoren
2025
Jahr
Abstract
AI proved to be a valuable growth prediction method, with clinically acceptable prediction errors averaging 1.49 mm for 45 hard tissue landmarks and 1.71 mm for 32 soft tissue landmarks. PLS accurately predicted landmarks with low variability. However, AI generally outperformed PLS, particularly for landmarks in the lower part of the craniofacial structure and soft tissue, where uncertainty is considerable.
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