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CC BY 4.0 · Rev Bras Ortop (Sao Paulo) 2024; 59(05): e689-e695
DOI: 10.1055/s-0044-1779317
Artigo Original
Coluna

Redes neurais convolucionais no diagnóstico de mielopatia cervical

Article in several languages: português | English
Murat Korkmaz 
1   Departamento de Ortopedia e Traumatologia, Istanbul Faculty of Medicine, Istanbul University, Istambul, Turquia
,
Hakan Yılmaz 
2   Departamento de Engenharia Médica, Faculty of Engineering, Karabuk University, Karabuk, Turquia
,
Merve Damla Korkmaz 
3   Departamento de Medicina Física e Reabilitação, Istanbul Kanuni Sultan Suleyman Training and Research Hospital, University of Health Sciences, Istambul, Turquia
,
Turgut Akgül 
1   Departamento de Ortopedia e Traumatologia, Istanbul Faculty of Medicine, Istanbul University, Istambul, Turquia
› Author Affiliations Suporte Financeiro Os autores declaram que a presente pesquisa não recebeu qualquer financiamento específico de agência de fomento dos setores públicos, comerciais ou sem fins lucrativos.
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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

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/)

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