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Aplicações da inteligência artificial no diagnóstico precoce de doenças peri-implantares : uma revisão de novas estratégias
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Abstract(s)
Introdução: As doenças peri-implantares constituem complicações relevantes na implantologia, podendo comprometer a longevidade dos implantes dentários. A deteção precoce destas patologias é fundamental para evitar a sua progressão e melhorar os resultados clínicos. Esta revisão scoping tem como objetivo mapear as estratégias mais recentes baseadas em Inteligência Artificial (IA) aplicadas à identificação precoce de doenças peri-implantares, avaliando o seu potencial de integração na prática clínica. Materiais e métodos: A revisão foi conduzida segundo as diretrizes PRISMA-ScR e registada no Open Science Framework (doi: https://doi.org/10.17605/OSF.IO/BPU2X). A pesquisa bibliográfica foi realizada nas bases PubMed e Web of Science. Foram incluídos estudos publicados entre 2017 e 2024 que abordassem a aplicação de IA na deteção ou prognóstico de doenças peri-implantares. O processo de seleção seguiu critérios de inclusão e exclusão previamente definidos, com triagem independente por dois revisores e resolução de divergências por um terceiro. A gestão dos artigos foi efetuada na plataforma Rayyan. Resultados: Foram inicialmente identificados 122 artigos, dos quais 25 foram excluídos por duplicação. Após triagem por título e resumo, 87 artigos foram excluídos por não cumprirem os critérios de elegibilidade. Dez estudos foram incluídos na análise final, abrangendo diferentes metodologias de IA, como machine learning e deep learning, aplicadas a dados clínicos e radiográficos. Os principais desfechos reportados foram a precisão na deteção automatizada de perda óssea marginal, previsão de risco para peri-implantite e apoio à decisão clínica. Conclusão: A inteligência artificial demonstra um potencial significativo para melhorar o diagnóstico precoce e o tratamento das doenças peri- implantares, promovendo uma abordagem mais preventiva e personalizada na implantologia. Contudo, são necessários estudos adicionais para padronizar metodologias e validar a aplicabilidade clínica destas ferramentas.
Introduction: Peri-implant diseases are significant complications in implantology, potentially compromising the longevity of dental implants. Early detection of these pathologies is essential to prevent their progression and improve clinical outcomes. This scoping review aims to map the most recent Artificial Intelligence (AI)-based strategies applied to the early identification of peri-implant diseases, assessing their potential for integration into clinical practice. Materials and methods: The review was conducted in accordance with PRISMA-ScR guidelines and registered in the Open Science Framework (doi: https://doi.org/10.17605/OSF.IO/BPU2X). A bibliographic search was carried out in the PubMed and Web of Science databases. Studies published between 2017 and 2024 addressing the application of AI in the detection or prognosis of peri-implant diseases were included. The selection process followed predefined inclusion and exclusion criteria, with independent screening by two reviewers and resolution of disagreements by a third. Article management was performed using the Rayyan platform. Results: A total of 122 articles were initially identified, of which 25 were excluded as duplicates. After screening titles and abstracts, 87 articles were excluded for not meeting the eligibility criteria. Ten studies were included in the final analysis, encompassing different AI methodologies such as machine learning and deep learning applied to clinical and radiographic data. The main outcomes reported were accuracy in the automated detection of marginal bone loss, risk prediction for peri-implantitis, and support for clinical decision-making. Conclusion: Artificial intelligence demonstrates significant potential to improve the early diagnosis and management of peri-implant diseases, promoting a more preventive and personalized approach in implantology. However, further studies are needed to standardize methodologies and validate the clinical applicability of these tools.
Introduction: Peri-implant diseases are significant complications in implantology, potentially compromising the longevity of dental implants. Early detection of these pathologies is essential to prevent their progression and improve clinical outcomes. This scoping review aims to map the most recent Artificial Intelligence (AI)-based strategies applied to the early identification of peri-implant diseases, assessing their potential for integration into clinical practice. Materials and methods: The review was conducted in accordance with PRISMA-ScR guidelines and registered in the Open Science Framework (doi: https://doi.org/10.17605/OSF.IO/BPU2X). A bibliographic search was carried out in the PubMed and Web of Science databases. Studies published between 2017 and 2024 addressing the application of AI in the detection or prognosis of peri-implant diseases were included. The selection process followed predefined inclusion and exclusion criteria, with independent screening by two reviewers and resolution of disagreements by a third. Article management was performed using the Rayyan platform. Results: A total of 122 articles were initially identified, of which 25 were excluded as duplicates. After screening titles and abstracts, 87 articles were excluded for not meeting the eligibility criteria. Ten studies were included in the final analysis, encompassing different AI methodologies such as machine learning and deep learning applied to clinical and radiographic data. The main outcomes reported were accuracy in the automated detection of marginal bone loss, risk prediction for peri-implantitis, and support for clinical decision-making. Conclusion: Artificial intelligence demonstrates significant potential to improve the early diagnosis and management of peri-implant diseases, promoting a more preventive and personalized approach in implantology. However, further studies are needed to standardize methodologies and validate the clinical applicability of these tools.
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Keywords
Inteligência artificial Peri-implantite Implante dentário Aprendizagem automática Aprendizagem profunda Perda óssea alveolar Artificial intelligence Peri-implantitis Dental implant Machine learning Deep learning Alveolar bone loss
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