Application of artificial intelligence in deep brain stimulation therapy_West China Medical Journal

Authors：

QI Rongxiao ,  LI Peng

Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

LI Peng, Email: lipeng_md@163.com

Keywords：

Deep brain stimulation; artificial intelligence; functional neurosurgery; Parkinson disease

DOI：

10.7507/1002-0179.202411015

Video：

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Deep brain stimulation (DBS), as a major branch of functional neurosurgery, has been widely used in the treatment of diseases such as Parkinson disease, primary tremor, and muscle tone disorder. It can effectively alleviate patients’ symptoms, reduce drug dependence, and minimize adverse drug reactions. In recent years, with the rapid development of artificial intelligence (AI) in the medical field, it has gradually penetrated into multiple aspects of DBS. This article reviews the current application status of AI technology in DBS therapy and explores its future development prospects, providing new solutions for significant improvement in patients’ quality of life.

Citation： QI Rongxiao, LI Peng. Application of artificial intelligence in deep brain stimulation therapy. West China Medical Journal, 2025, 40(3): 486-489. doi: 10.7507/1002-0179.202411015 Copy

1.	Asir B, Boscutti A, Fenoy AJ, et al. Deep brain stimulation (DBS) in treatment-resistant depression (TRD): hope and concern. Adv Exp Med Biol, 2024, 1456: 161-186.
2.	Lozano AM, Lipsman N, Bergman H, et al. Deep brain stimulation: current challenges and future directions. Nat Rev Neurol, 2019, 15(3): 148-160.
3.	冼文彪, 陈玲. 帕金森病脑深部电刺激治疗. 中国实用内科杂志, 2019, 39(9): 778-782.
4.	Peralta M, Jannin P, Baxter JSH. Machine learning in deep brain stimulation: a systematic review. Artif Intell Med, 2021, 122: 102198.
5.	Samuel, AL. Some studies in machine learning using the game of checkers. IBM J Res Develop, 1959, 3(3): 210-229.
6.	Senders JT, Zaki MM, Karhade AV, et al. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien), 2018, 160(1): 29-38.
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8.	Buchlak QD, Esmaili N, Leveque JC, et al. Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review. Neurosurg Rev, 2020, 43(5): 1235-1253.
9.	Senders JT, Arnaout O, Karhade AV, et al. Natural and Artificial Intelligence in neurosurgery: a systematic review. Neurosurgery, 2018, 83(2): 181-192.
10.	Konar A. Artifcial intelligence and soft computing behavioral and cognitive modeling of the human brain. Boca Raton: CRC Press, 1999.
11.	Myers A. Stanford’s John McCarthy, seminal fgure of artifcial intelligence, dies at 84. California: Stanford University, 2011.
12.	Russell SJ, Norvig P. Artifcial intelligence-a modern approach. 3rd ed. New Jersey: Pearson Education Inc. , 2010.
13.	Shamir RR, Dolber T, Noecker AM, et al. Machine learning approach to optimizing combined stimulation and medication therapies for Parkinson’s disease. Brain Stimul, 2015, 8(6): 1025-1032.
14.	Suppa A, Asci F, Costantini G, et al. Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson’s disease: a machine-learning voice analysis. Front Neurol, 2023, 14: 1267360.
15.	Jiang R, Chazot P, Pavese N, et al. Private facial prediagnosis as an edge service for Parkinson’s DBS treatment valuation. IEEE J Biomed Health Inform, 2022, 26(6): 2703-2713.
16.	Wan KR, Maszczyk T, See AAQ, et al. A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease. Clin Neurophysiol, 2019, 130(1): 145-154.
17.	Falkenberg JH, McNames J, Favre J, et al. Automatic analysis and visualization of microelectrode recording trajectories to the subthalamic nucleus: preliminary results. Stereotact Funct Neurosurg, 2006, 84(1): 35-45.
18.	Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods, 2020, 17(3): 261-272.
19.	Li KHC, White FA, Tipoe T, et al. The current state of mobile phone apps for monitoring heart rate, heart rate variability, and atrial fibrillation: narrative review. JMIR Mhealth Uhealth, 2019, 7(2): e11606.
20.	Boutet A, Hancu I, Saha U, et al. 3-Tesla MRI of deep brain stimulation patients: safety assessment of coils and pulse sequences. J Neurosurg, 2019, 132(2): 586-594.
21.	Rathore S, Habes M, Iftikhar MA, et al. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage, 2017, 155: 530-548.
22.	Cagnan H, Dolan K, He X, et al. Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity. J Neural Eng, 2011, 8(4): 046006.
23.	Shah SA, Tinkhauser G, Chen CC, et al. Parkinsonian tremor detection from subthalamic nucleus local field potentials for closed-loop deep brain stimulation. Annu Int Conf IEEE Eng Med Biol Soc, 2018, 2018: 2320-2324.
24.	Koch M, Geraedts V, Wang H, et al. Automated machine learning for EEG-based classification of Parkinson’s disease patients//Proceedings of the 2019 IEEE International Conference on Big Data (Big Data). Los Angeles: IEEE, 2019: 4845-4852.
25.	Ciecierski KA, Mandat T. Unsupervised machine learning in classification of neurobiological data. Berlin: Springer, 2019: 203-212.
26.	Valsky D, Blackwell KT, Tamir I, et al. Real-time machine learning classification of pallidal borders during deep brain stimulation surgery. J Neural Eng, 2020, 17(1): 016021.
27.	Karthick PA, Wan KR, An Qi AS, et al. Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson’s disease using microelectrode recordings and wavelet packet features. J Neurosci Methods, 2020, 343: 108826.
28.	Hosny M, Zhu M, Gao W, et al. Deep convolutional neural network for the automated detection of subthalamic nucleus using MER signals. J Neurosci Methods, 2021, 356: 109145.
29.	Martin T, Gilmore G, Haegelen C, et al. Adapting the listening time for micro-electrode recordings in deep brain stimulation interventions. Int J Comput Assist Radiol Surg, 2021, 16(8): 1371-1379.
30.	Mesko B. The role of artifcial intelligence in precision medicine. Expert Rev Precis Med Drug Dev, 2017, 2(5): 239-241.

1. Asir B, Boscutti A, Fenoy AJ, et al. Deep brain stimulation (DBS) in treatment-resistant depression (TRD): hope and concern. Adv Exp Med Biol, 2024, 1456: 161-186.
2. Lozano AM, Lipsman N, Bergman H, et al. Deep brain stimulation: current challenges and future directions. Nat Rev Neurol, 2019, 15(3): 148-160.
3. 冼文彪, 陈玲. 帕金森病脑深部电刺激治疗. 中国实用内科杂志, 2019, 39(9): 778-782.
4. Peralta M, Jannin P, Baxter JSH. Machine learning in deep brain stimulation: a systematic review. Artif Intell Med, 2021, 122: 102198.
5. Samuel, AL. Some studies in machine learning using the game of checkers. IBM J Res Develop, 1959, 3(3): 210-229.
6. Senders JT, Zaki MM, Karhade AV, et al. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien), 2018, 160(1): 29-38.
7. Celtikci E. A systematic review on machine learning in neurosurgery: the future of decision-making in patient care. Turk Neurosurg, 2018, 28(2): 167-173.
8. Buchlak QD, Esmaili N, Leveque JC, et al. Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review. Neurosurg Rev, 2020, 43(5): 1235-1253.
9. Senders JT, Arnaout O, Karhade AV, et al. Natural and Artificial Intelligence in neurosurgery: a systematic review. Neurosurgery, 2018, 83(2): 181-192.
10. Konar A. Artifcial intelligence and soft computing behavioral and cognitive modeling of the human brain. Boca Raton: CRC Press, 1999.
11. Myers A. Stanford’s John McCarthy, seminal fgure of artifcial intelligence, dies at 84. California: Stanford University, 2011.
12. Russell SJ, Norvig P. Artifcial intelligence-a modern approach. 3rd ed. New Jersey: Pearson Education Inc. , 2010.
13. Shamir RR, Dolber T, Noecker AM, et al. Machine learning approach to optimizing combined stimulation and medication therapies for Parkinson’s disease. Brain Stimul, 2015, 8(6): 1025-1032.
14. Suppa A, Asci F, Costantini G, et al. Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson’s disease: a machine-learning voice analysis. Front Neurol, 2023, 14: 1267360.
15. Jiang R, Chazot P, Pavese N, et al. Private facial prediagnosis as an edge service for Parkinson’s DBS treatment valuation. IEEE J Biomed Health Inform, 2022, 26(6): 2703-2713.
16. Wan KR, Maszczyk T, See AAQ, et al. A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease. Clin Neurophysiol, 2019, 130(1): 145-154.
17. Falkenberg JH, McNames J, Favre J, et al. Automatic analysis and visualization of microelectrode recording trajectories to the subthalamic nucleus: preliminary results. Stereotact Funct Neurosurg, 2006, 84(1): 35-45.
18. Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods, 2020, 17(3): 261-272.
19. Li KHC, White FA, Tipoe T, et al. The current state of mobile phone apps for monitoring heart rate, heart rate variability, and atrial fibrillation: narrative review. JMIR Mhealth Uhealth, 2019, 7(2): e11606.
20. Boutet A, Hancu I, Saha U, et al. 3-Tesla MRI of deep brain stimulation patients: safety assessment of coils and pulse sequences. J Neurosurg, 2019, 132(2): 586-594.
21. Rathore S, Habes M, Iftikhar MA, et al. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage, 2017, 155: 530-548.
22. Cagnan H, Dolan K, He X, et al. Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity. J Neural Eng, 2011, 8(4): 046006.
23. Shah SA, Tinkhauser G, Chen CC, et al. Parkinsonian tremor detection from subthalamic nucleus local field potentials for closed-loop deep brain stimulation. Annu Int Conf IEEE Eng Med Biol Soc, 2018, 2018: 2320-2324.
24. Koch M, Geraedts V, Wang H, et al. Automated machine learning for EEG-based classification of Parkinson’s disease patients//Proceedings of the 2019 IEEE International Conference on Big Data (Big Data). Los Angeles: IEEE, 2019: 4845-4852.
25. Ciecierski KA, Mandat T. Unsupervised machine learning in classification of neurobiological data. Berlin: Springer, 2019: 203-212.
26. Valsky D, Blackwell KT, Tamir I, et al. Real-time machine learning classification of pallidal borders during deep brain stimulation surgery. J Neural Eng, 2020, 17(1): 016021.
27. Karthick PA, Wan KR, An Qi AS, et al. Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson’s disease using microelectrode recordings and wavelet packet features. J Neurosci Methods, 2020, 343: 108826.
28. Hosny M, Zhu M, Gao W, et al. Deep convolutional neural network for the automated detection of subthalamic nucleus using MER signals. J Neurosci Methods, 2021, 356: 109145.
29. Martin T, Gilmore G, Haegelen C, et al. Adapting the listening time for micro-electrode recordings in deep brain stimulation interventions. Int J Comput Assist Radiol Surg, 2021, 16(8): 1371-1379.
30. Mesko B. The role of artifcial intelligence in precision medicine. Expert Rev Precis Med Drug Dev, 2017, 2(5): 239-241.

West China Medical Journal

Application of artificial intelligence in deep brain stimulation therapy

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