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2025, Número 3

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Acta Ortop Mex 2025; 39 (3)


Aplicaciones de aprendizaje profundo en ortopedia: una revisión sistemática y futuras direcciones

González-Pola R, Herrera-Lozano A, Graham-Nieto L, Zermeño-García G

Idioma: Ingles.
Referencias bibliográficas: 88
Paginas: 152-163
Archivo PDF: 263.41 Kb.


RESUMEN

Introducción: la inteligencia artificial (IA) y deep learning en ortopedia han ganado un gran interés en los últimos años. En estudios anteriores, se han mostrado diferentes aplicaciones, desde la evaluación radiográfica hasta el diagnóstico de tumores óseos. El propósito de esta revisión es analizar literatura actual sobre IA y deep learning para identificar las herramientas más utilizadas en los campos de evaluación, resultados, imágenes y ciencias básicas. Material y métodos: se realizaron búsquedas en PubMed, EMBASE y Google Scholar desde enero de 2020 hasta el 31 de octubre de 2023. Se identificaron 862 estudios, de los cuales 595 fueron incluidos. Se incluyeron un total de 281 estudios sobre evaluación radiográfica, 102 sobre cirugía de columna, 95 sobre evaluación de resultados, 84 sobre educación ortopédica y 33 aplicaciones de ciencias básicas. Los resultados primarios fueron la precisión diagnóstica, diseño del estudio y estándares de presentación de informes en la literatura. Las estimaciones se agruparon mediante un metaanálisis de efectos aleatorios. Resultados: se utilizaron 53 métodos de imagen diferentes para medir los aspectos radiográficos. Se utilizaron un total de 185 algoritmos diferentes de aprendizaje automático, siendo la arquitectura de red neuronal convolucional la más común (73%). Mejorar la precisión y la velocidad del diagnóstico fueron los resultados más reportados (62%). Conclusión: la heterogeneidad fue alta entre los estudios y se observó una amplia variación en la metodología, terminología y medidas de resultados. Esto puede llevar a una sobreestimación de la precisión diagnóstica de los algoritmos para imagenología. Existe una necesidad inmediata de desarrollar directrices específicas para la IA que proporcionen orientación sobre cuestiones clave.


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