PREDIÇÃO DE FALHAS EM IMPLANTES DENTÁRIOS USANDO MODELOS DE APRENDIZADO DE MÁQUINA: UMA REVISÃO INTEGRATIVA
DOI:
https://doi.org/10.54899/dcs.v22i81.3012Palavras-chave:
Implantes Dentários, Aprendizado de Máquina, Inteligência Artificial, Predição de Falha, PrognósticoResumo
Apesar da elevada taxa de sucesso dos implantes dentários, suas falhas ainda representam um desafio clínico. Fatores como condições do paciente, do implante, técnica cirúrgica e componentes protéticos estão associados às falhas quando analisados por métodos estatísticos convencionais, que, no entanto, possuem limitações diante de interações complexas. A inteligência artificial (IA), por meio de modelos de aprendizado de máquina (AM), surge como alternativa promissora para prever falhas, complicações e prognósticos, favorecendo um planejamento mais individualizado e eficiente. Este estudo realizou uma revisão integrativa da literatura publicada entre 2018 e 2025, a busca nas bases de dados resultou em 2.459 registros, sendo 27 estudos incluídos após triagem e análise completa, conforme o diagrama PRISMA adaptado. Os modelos mais frequentes foram redes neurais profundas (48%), random forest (30%) e máquinas de vetores de suporte (22%). As principais variáveis de entrada incluíram características do implante (100%), dados demográficos dos pacientes (85%) e fatores clínicos (78%). As métricas de desempenho variaram entre 70% e 96,13%, com destaque para modelos com AUROC acima de 0,90 na predição de falhas e superior a 0,97 na detecção de peri-implantite. No entanto, limitações relevantes foram observadas, como a baixa taxa de validação externa, heterogeneidade metodológica e ausência de padronização dos desfechos. Conclui-se que os modelos de ML demonstram desempenho promissor na predição de falhas em implantes dentários, embora haja necessidade de estudos multicêntricos com validação externa, maior padronização e foco na aplicação clínica, a fim de viabilizar sua incorporação prática na odontologia.
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