Resumo
Objetivo: Apresentar e sumarizar o conceito de dispositivos de neuromodulação inteligentes e de malha fechada, que visam identificar as assinaturas neurais específicas da dor crônica e cancelá-las seletivamente. Revisão bibliográfica: Esta revisão narrativa explora uma abordagem revolucionária na interseção da neurociência e da inteligência artificial, tratando a dor crônica como uma patologia de circuito neural. Discute-se como os algoritmos de machine learning podem decodificar dados neurais complexos para isolar os sinais de dor em tempo real, permitindo a identificação de um biomarcador objetivo. A partir dessa decodificação, a neuroestimulação direcionada pode fornecer intervenções precisas para interromper estes sinais patológicos, operando em um sistema de malha fechada (sentir-decodificar-atuar) que é mais eficaz e seguro do que as terapias de malha aberta convencionais. Esta abordagem representa a transição de tratamentos sistêmicos para uma terapia adaptativa e personalizada. Considerações finais: Pode-se considerar que este paradigma impulsionado pela IA promete uma mudança fundamental no tratamento da dor. Ao oferecer uma terapia personalizada e específica, ele representa uma potencial solução para condições de dor intratável, redefinindo o tratamento de uma condição que afeta milhões de pessoas e transformando um sintoma em um sinal biológico tratável.
Referências
APKHARIAN AV. Human Brain Imaging Studies of Chronic Pain: Translational Opportunities. In: Kruger L, Light AR, editors. Translational Pain Research: From Mouse to Man. Boca Raton (FL): CRC Press/Taylor & Francis; 2010; 239-251.
COHEN SP, et al. Chronic pain: an update on burden, best practices, and new advances. The Lancet, 2021; 397(10289): 2082-2097.
DEER TR, et al. The Neurostimulation Appropriateness Consensus Committee (NACC) recommendations for implantable neurostimulation devices. Neuromodulation, 2017; 20(3): 195-217.
DREW L. The ethics of brain-computer interfaces. Nature, 2019; 571: 15-17.
GBD 2016 DALY AND HALE COLLABORATORS. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet, 2017; 390(10100): 1260-1344.
GBD 2021 LOW BACK PAIN COLLABORATORS. Global, regional, and national burden of low back pain, 1990–2020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. The Lancet Rheumatology, 2023; 5: 316-29.
HAAG L, et al. Ethical gaps in closed-loop neurotechnology: a scoping review. npj Digital Medicine, 2025; 8: 11.
HASHMI JA, et al. Shape-shifting pain: chronification of back pain shifts brain representation from nociceptive to emotional circuits. Brain, 2013; 136(9): 2751-2768.
HECK CN, et al. Two-year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: final results of the RNS System Pivotal trial. Epilepsia, 2014; 55(3): 432-441.
KNOTKOVA H, et al. Neuromodulation for chronic pain. The Lancet, 2021; 397(10289): 2111-2124.
KUNER R e FLOR H. Structural plasticity and reorganisation in chronic pain. Nature Reviews Neuroscience, 2016; 18(1): 20-30.
LAM CM, et al. Advances in spinal cord stimulation. Bioengineering, 2023; 10(9): 1004.
LEGON W, et al. Transcranial focused ultrasound neuromodulation of the human primary motor cortex. Nature Neuroscience, 2014; 17(2): 322-329.
LITTLE S, et al. Adaptive deep-brain stimulation in advanced Parkinson's disease. Annals of Neurology, 2013; 74(3): 449-457.
PLONER M, et al. Are there pain-specific brain representations? A meta-analysis of brain imaging studies. Pain, 2017; 158(7): 1217-1221.
RAZA A, et al. Closed-loop spinal cord stimulation: a review of the evidence and future directions. Neuromodulation, 2023; 26(1): 15-24.
SHIRVALKAR P, et al. First-in-human prediction of chronic pain state using intracranial neural biomarkers. Nature Neuroscience, 2023; 26(6): 1090-1099.
SHOU G, et al. Machine learning in pain research. Pain Medicine, 2020; 21(11): 2821-2838.
STUBHAUG A, et al. The costs of chronic pain: long-term estimates. European Journal of Pain, 2024; 28: 960-977.
VOWLES KE, et al. Rates of opioid misuse, abuse, and addiction in chronic pain: a systematic review and meta-analysis. Pain, 2015; 156(4): 569-576.
WAGER TD, et al. An fMRI-based neurologic signature of physical pain. The New England Journal of Medicine, 2013; 368(15): 1388-1397.
WOOLF CJ. Central Sensitization: Implications for the Diagnosis and Treatment of Pain. Pain, 2011; 152(3): 2-15.
ZEBHAUSER PT, et al. Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review. Pain. 2023; 164(6): 1200-1221.
