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人工智能在利用PET/FDG阐明长期新冠患者脑功能方面的变革性作用。

Artificial Intelligence's Transformative Role in Illuminating Brain Function in Long COVID Patients Using PET/FDG.

作者信息

Rudroff Thorsten

机构信息

Department of Health and Human Physiology, University of Iowa, Iowa City, IA 52242, USA.

Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA.

出版信息

Brain Sci. 2024 Jan 10;14(1):73. doi: 10.3390/brainsci14010073.

Abstract

Cutting-edge brain imaging techniques, particularly positron emission tomography with Fluorodeoxyglucose (PET/FDG), are being used in conjunction with Artificial Intelligence (AI) to shed light on the neurological symptoms associated with Long COVID. AI, particularly deep learning algorithms such as convolutional neural networks (CNN) and generative adversarial networks (GAN), plays a transformative role in analyzing PET scans, identifying subtle metabolic changes, and offering a more comprehensive understanding of Long COVID's impact on the brain. It aids in early detection of abnormal brain metabolism patterns, enabling personalized treatment plans. Moreover, AI assists in predicting the progression of neurological symptoms, refining patient care, and accelerating Long COVID research. It can uncover new insights, identify biomarkers, and streamline drug discovery. Additionally, the application of AI extends to non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), which have shown promise in alleviating Long COVID symptoms. AI can optimize treatment protocols by analyzing neuroimaging data, predicting individual responses, and automating adjustments in real time. While the potential benefits are vast, ethical considerations and data privacy must be rigorously addressed. The synergy of AI and PET scans in Long COVID research offers hope in understanding and mitigating the complexities of this condition.

摘要

前沿的脑成像技术,特别是氟脱氧葡萄糖正电子发射断层扫描(PET/FDG),正与人工智能(AI)结合使用,以揭示与长新冠相关的神经症状。人工智能,尤其是卷积神经网络(CNN)和生成对抗网络(GAN)等深度学习算法,在分析PET扫描、识别细微的代谢变化以及更全面地了解长新冠对大脑的影响方面发挥着变革性作用。它有助于早期检测异常的脑代谢模式,从而制定个性化的治疗方案。此外,人工智能有助于预测神经症状的进展,改善患者护理,并加速长新冠的研究。它可以揭示新的见解,识别生物标志物,并简化药物发现过程。此外,人工智能的应用还扩展到非侵入性脑刺激技术,如经颅直流电刺激(tDCS),该技术在缓解长新冠症状方面已显示出前景。人工智能可以通过分析神经影像数据、预测个体反应并实时自动调整来优化治疗方案。虽然潜在益处巨大,但必须严格解决伦理考量和数据隐私问题。人工智能与PET扫描在长新冠研究中的协同作用为理解和缓解这种疾病的复杂性带来了希望。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/292d/10813353/ef655606e886/brainsci-14-00073-g001.jpg

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