MACHINE LEARNING IN IMPLANT DENTISTRY:
FROM TOMOGRAPHIC ANALYSIS TO PERIO-IMPLANTITIS PREDICTION
DOI:
https://doi.org/10.36557/2674-9432.2026v5n1p3118-3125Palavras-chave:
Artificial Intelligence, Implant Dentistry, Cone-Beam Computed Tomography, Peri-implantitis, Machine LearningResumo
The incorporation of Artificial Intelligence (AI) and Machine Learning (ML) in implant dentistry represents a disruptive advancement in digital dentistry, enabling a more predictable and personalized clinical practice. The present study analyzes the applicability of these technologies throughout the implant treatment workflow, from automated tomographic analysis to long-term biological monitoring.
In diagnostic imaging, deep learning models demonstrate high accuracy in the segmentation of critical anatomical structures such as the mandibular canal and maxillary sinuses, as well as the automated identification of implant systems and mitigation of metallic artifacts in cone-beam computed tomography (CBCT) [3,4]. During the surgical phase, AI-assisted dynamic navigation systems optimize the three-dimensional positioning of implants in real time [12].
Recent literature also highlights the role of predictive algorithms in the early detection of marginal bone loss and in multifactorial risk analysis for peri-implantitis [1]. The integration between human clinical judgment and algorithmic precision emerges as a key factor in improving surgical safety, treatment predictability, and long-term implant success.
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Referências
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Copyright (c) 2026 Ana Luisa de Castro e Silva, Jennifer Vera Santos Gumert, Lahis Signor Caumo, Camila de Souza Nunes, Larissa Caroline Cayres Pereira, Jéssica Luiza Feitosa Monteiro Alves, Nathália Félix de Leiros Ferreira, Sabrina Lima Dantas Nóbrega, Maiana Vaz Moreira, Andreza Calazans Rodrigues
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