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AI in Cephalometric Diagnosis Accuracy by JENNY PAOLA MUNEVAR RIAÑO is a document available to read on EtoBox.
This study evaluates the reliability, accuracy, and time efficiency of AI-based software for cephalometric diagnosis compared to conventional methods using 408 lateral cephalometries. Results indicated statistically significant differences in landmark positioning accuracy among the methods, though most were not clinically significant, with the semi-automatic AI option being more accurate and faster than conventional techniques. Further research is suggested to validate AI
- Author
- JENNY PAOLA MUNEVAR RIAÑO
- Language
- EN