Uso de aprendizaje automático para la identificación de degeneración y hernia discal. ¿Es superior a la valoración radiológica tradicional?
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5. Pérez-Fontalvo NM, De Arco-Aragón MA, Jimenez-García JDC, Lozada-Martinez ID. Molecular and computational research in low- and middle-income countries: Development is close at hand. J Taibah Univ Med Sci. 2021; 16(6):948-949. https://doi.org/10.1016/j.jtumed.2021.06.010
2. Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battié MC, et al. ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur Spine J. 2017; 26(5):1374-1383. https://doi.org/10.1007/s00586-017-4956-3
3. Compte R, Granville Smith I, Isaac A, Danckert N, McSweeney T, Liantis P, et al. Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis. Eur Spine J. 2023; 32(11):3764-3787. https://doi.org/10.1007/s00586-023-07718-0
4. D'Antoni F, Russo F, Ambrosio L, Bacco L, Vollero L, Vadalà G, et al. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review. Int J Environ Res Public Health. 2022; 19(10):5971. https://doi.org/10.3390/ijerph1910597
5. Pérez-Fontalvo NM, De Arco-Aragón MA, Jimenez-García JDC, Lozada-Martinez ID. Molecular and computational research in low- and middle-income countries: Development is close at hand. J Taibah Univ Med Sci. 2021; 16(6):948-949. https://doi.org/10.1016/j.jtumed.2021.06.010