Redes neurais convolucionais no diagnóstico de mielopatia cervical

Resumo

Objetivo As tecnologias de inteligência artificial são cada vez mais utilizadas em cirurgias de coluna como ferramentas diagnósticas. O objetivo do presente estudo foi avaliar a eficácia das redes neurais convolucionais no diagnóstico da mielopatia cervical (MC) em comparação à ressonância magnética (RM) cervical convencional.

Métodos O presente estudo foi transversal, descritivo e analítico. Cento e vinte e cinco participantes com diagnóstico clínico e radiológico de MC foram incluídos no estudo. Foram utilizadas imagens de RM sagital e axial em sequência ponderada em T2 da coluna cervical. Todas as imagens foram obtidas em 8 bits/pixel em duas categorias diferentes (MC e normal), tanto em vistas axiais quanto sagitais.

Resultados A validação transversal tripla evitou o sobreajuste (overfitting) durante o processo de treinamento. Duzentas e quarenta e duas imagens foram utilizadas para treinamento e teste do modelo criado para vistas axiais, que apresentou 97,44% de sensibilidade e 97,56% de especificidade. Duzentas e quarenta e nove imagens foram utilizadas para treinamento e teste do modelo criado para vistas sagitais, que apresentou 97,50% de sensibilidade e 97,67% de especificidade. Após o treinamento, a acurácia média foi de 96,7% (±1,53) para a vista axial e de 97,19% (±1,2) para a vista sagital.

Conclusão O deep learning (DL) apresentou grande melhora, especialmente na cirurgia de coluna. Observamos que a tecnologia de DL trabalha com maior acurácia do que em outros estudos na literatura para diagnóstico de MC.

Palavras-chave

aprendizado profundo - imagem por ressonância magnética - mielopatia - redes neurais artificiais - vértebras cervicais

Trabalho desenvolvido no Departamento de Medicina Física de Reabilitação, University of Health Sciences, Istanbul Kanuni Sultan Suleyman Training and Research Hospital, Istambul, Turquia.

Publication History

Received: 08 March 2023

Accepted: 05 May 2023

Article published online:
07 December 2024

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