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AI and deep brain stimulation: what have we learned?

Deep brain stimulation (DBS) is a well-established approach for treating movement disorders such as Parkinson disease, dystonia and essential tremor. However, the outcomes are variable, and researchers are now exploring artificial intelligence-based strategies to help improve DBS procedures.

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References

  1. Roy, A. G., Conjeti, S., Navab, N., Wachinger, C. & Alzheimer’s Disease Neuroimaging Initiative. QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. Neuroimage 186, 713–727 (2019).

    Article  Google Scholar 

  2. Henschel, L. et al. FastSurfer - a fast and accurate deep learning based neuroimaging pipeline. Neuroimage 219, 117012 (2020).

    Article  PubMed  Google Scholar 

  3. Ronneberger, O., Fischer, P. & Brox, T. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science vol. 9351, 234–241 (Springer, 2015).

  4. Milletari, F. et al. Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Underst. 164, 92–102 (2017).

    Article  Google Scholar 

  5. Lin, H. et al. Low-field magnetic resonance image enhancement via stochastic image quality transfer. Med. Image Anal. 87, 102807 (2023).

    Article  PubMed  Google Scholar 

  6. Jurek, J. et al. Supervised denoising of diffusion-weighted magnetic resonance images using a convolutional neural network and transfer learning. Biocybern. Biomed. Eng. 43, 206–232 (2023).

    Article  Google Scholar 

  7. Malekmohammadi, M. et al. Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets. J. Neural Eng. 19, 046014 (2022).

    Article  Google Scholar 

  8. Roediger, J. et al. StimFit — a data-driven algorithm for automated deep brain stimulation programming. Mov. Disord. 37, 574–584 (2022).

    Article  PubMed  Google Scholar 

  9. Boutet, A. et al. Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning. Nat. Commun. 12, 3043 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Little, S. et al. Adaptive deep brain stimulation for Parkinson’s disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting. J. Neurol. Neurosurg. Psychiatry 87, 1388–1389 (2016).

    Article  PubMed  Google Scholar 

Download references

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Correspondence to Patricia Limousin.

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Limousin, P., Akram, H. AI and deep brain stimulation: what have we learned?. Nat Rev Neurol 19, 451–452 (2023). https://doi.org/10.1038/s41582-023-00836-9

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