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Resumo(s)
A regeneração óssea guiada representa uma abordagem essencial em Cirurgia Oral e Implantologia, permitindo reconstruir defeitos ósseos e melhorar as condições anatómicas necessárias à reabilitação implantar. A previsibilidade destes procedimentos depende de múltiplos fatores biológicos, cirúrgicos e materiais, incluindo a estabilidade do enxerto e da membrana, a manutenção do espaço, a vascularização local, o controlo dos tecidos moles e as características individuais do doente.
O objetivo desta dissertação é analisar de que forma a inteligência artificial pode emergir como ferramenta de apoio à decisão terapêutica na regeneração óssea guiada, contribuindo para um planeamento mais rigoroso, individualizado e previsível. Neste contexto, a IA apresenta potencial na segmentação de imagens radiográficas, na avaliação tridimensional de defeitos ósseos, na monitorização dos resultados regenerativos, na previsão de complicações e no desenvolvimento de soluções digitais, nomeadamente através do design assistido e da impressão 3D.
Apesar do seu potencial, a integração clínica da IA na regeneração óssea guiada ainda exige validação científica robusta, dados padronizados, transparência algorítmica, proteção de dados e supervisão clínica. Assim, a IA deve ser entendida como uma ferramenta complementar ao raciocínio do médico dentista, capaz de reforçar a previsibilidade terapêutica, mas não de substituir a decisão clínica.
Guided bone regeneration is an essential approach in Oral Surgery and Implantology, allowing the reconstruction of bone defects and improving the anatomical conditions required for implant rehabilitation. The predictability of these procedures depends on multiple biological, surgical and material-related factors, including graft and membrane stability, space maintenance, local vascularisation, soft tissue management and individual patient characteristics. The aim of this dissertation is to analyse how artificial intelligence may emerge as a tool to support therapeutic decision-making in guided bone regeneration, contributing to more accurate, individualised and predictable planning. In this context, AI shows potential in radiographic image segmentation, threedimensional assessment of bone defects, monitoring of regenerative outcomes, prediction of complications and development of personalised digital solutions, particularly through assisted design and 3D printing. Despite its potential, the clinical integration of AI in guided bone regeneration still requires robust scientific validation, standardised data, algorithmic transparency, data protection and clinical supervision. Therefore, AI should be understood as a complementary tool to the dentist’s clinical reasoning, capable of strengthening therapeutic predictability, but not replacing clinical decision-making.
Guided bone regeneration is an essential approach in Oral Surgery and Implantology, allowing the reconstruction of bone defects and improving the anatomical conditions required for implant rehabilitation. The predictability of these procedures depends on multiple biological, surgical and material-related factors, including graft and membrane stability, space maintenance, local vascularisation, soft tissue management and individual patient characteristics. The aim of this dissertation is to analyse how artificial intelligence may emerge as a tool to support therapeutic decision-making in guided bone regeneration, contributing to more accurate, individualised and predictable planning. In this context, AI shows potential in radiographic image segmentation, threedimensional assessment of bone defects, monitoring of regenerative outcomes, prediction of complications and development of personalised digital solutions, particularly through assisted design and 3D printing. Despite its potential, the clinical integration of AI in guided bone regeneration still requires robust scientific validation, standardised data, algorithmic transparency, data protection and clinical supervision. Therefore, AI should be understood as a complementary tool to the dentist’s clinical reasoning, capable of strengthening therapeutic predictability, but not replacing clinical decision-making.
Descrição
Dissertação para obtenção do grau de Mestre no Instituto Universitário Egas Moniz
Palavras-chave
Regeneração óssea guiada Inteligência artificial Cirurgia Oral Implantologia
