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Comparación de modelos de inteligencia artificial para la segmentación automática de los conductos y ramas
Hanyang Man1, Shikun Ma2, Huifeng Luo1
1Department of Periodontology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University and Shandong Key Laboratory of Oral Tissue Regeneration and Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration and Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, Shandong, China.
El modelo de aprendizaje profundo 3D UX-Net, mejorado con posprocesamiento anatómico, logró una segmentación superior del conducto mandibular y sus ramas en exploraciones CBCT en comparación con Swin UNETR y UNETR. Este enfoque de IA mejora significativamente la velocidad y la precisión para el análisis de imágenes dentales.
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