Subscribe to RSS
DOI: 10.1055/a-2753-6166
The Role of Artificial Intelligence in Deep Brain Stimulation
Authors
Abstract
Deep brain stimulation (DBS) is a highly effective treatment for movement disorders like Parkinson's disease, essential tremor, and dystonia. However, the current multidisciplinary workflow for implanting and programming DBS is often complex, which can lead to improperly placed leads, suboptimal symptom management, and increased procedure time, ultimately resulting in poor patient outcomes. There is a pressing need for a more streamlined, accurate, reproducible, and personalized approach to DBS therapy. Artificial intelligence (AI), which can analyze complex data and identify patterns with remarkable speed, holds significant promise as a tool to address these challenges. This narrative review explores the current and future applications of AI in improving the entire DBS workflow, from surgical planning and lead placement to postoperative programming, with the goal of enhancing clinical efficiency and achieving better, more personalized outcomes for patients with movement disorders.
Publication History
Received: 31 August 2025
Accepted: 11 November 2025
Accepted Manuscript online:
25 November 2025
Article published online:
12 December 2025
© 2025. Thieme. All rights reserved.
Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA
-
References
- 1 Alhejaily A-M. Artificial intelligence in healthcare (review). Biomedical Reports 2024; 22 (01)
- 2 McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol 1990; 52 (1–2): 99-115 , discussion 73–97
- 3 Turing AM. Computing machinery and intelligence. Mind 1950; 49: 433-460
- 4 Feigenbaum EA. Knowledge engineering. The applied side of artificial intelligence. Ann N Y Acad Sci 1984; 426 (01) 91-107
- 5 Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986; 323 (6088) 533-536
- 6 Campbell M, Hoane AJ, Hsu F. Deep blue. Artif Intell 2002; 134 (1–2): 57-83
- 7 Ferrucci D, Brown E, Chu-Carroll J. et al. Building Watson: an overview of the DeepQA project. AI Mag 2010; 31 (03) 59-79
- 8 Silver D, Huang A, Maddison CJ. et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016; 529 (7587) 484-489
- 9 Ali O, Abdelbaki W, Shrestha A, Elbasi E, Alryalat MAA, Dwivedi YK. A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge 2023; 8 (01) 100333
- 10 Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. 2020 A Review of Challenges and Opportunities in Machine Learning for Health. AMIA Summits on Translational Science Proceedings, [online] 2020, p.191. Accessed November 26, 2025 at: https://pmc.ncbi.nlm.nih.gov/articles/pmc7233077/
- 11 Jiang F, Jiang Y, Zhi H. et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2 (04) 230-243
- 12 Obeso JA, Olanow CW, Rodriguez-Oroz MC, Krack P, Kumar R, Lang AE. Deep-Brain Stimulation for Parkinson's Disease Study Group. Deep-brain stimulation of the subthalamic nucleus or the pars interna of the globus pallidus in Parkinson's disease. N Engl J Med 2001; 345 (13) 956-963
- 13 Benabid AL, Pollak P, Gao D. et al. Chronic electrical stimulation of the ventralis intermedius nucleus of the thalamus as a treatment of movement disorders. J Neurosurg 1996; 84 (02) 203-214
- 14 Nuttin B, Cosyns P, Demeulemeester H, Gybels J, Meyerson B. Electrical stimulation in anterior limbs of internal capsules in patients with obsessive-compulsive disorder. Lancet 1999; 354 (9189) 1526
- 15 Vidailhet M, Vercueil L, Houeto JL. et al; French Stimulation du Pallidum Interne dans la Dystonie (SPIDY) Study Group. Bilateral deep-brain stimulation of the globus pallidus in primary generalized dystonia. N Engl J Med 2005; 352 (05) 459-467
- 16 Schrock LE, Mink JW, Woods DW. et al; Tourette Syndrome Association International Deep Brain Stimulation (DBS) Database and Registry Study Group. Tourette syndrome deep brain stimulation: a review and updated recommendations. Mov Disord 2015; 30 (04) 448-471
- 17 McIntyre CC, Savasta M, Kerkerian-Le Goff L, Vitek JL. Uncovering the mechanism(s) of action of deep brain stimulation: activation, inhibition, or both. Clin Neurophysiol 2004; 115 (06) 1239-1248
- 18 Hashimoto T, Elder CM, Okun MS, Patrick SK, Vitek JL. Stimulation of the subthalamic nucleus changes the firing pattern of pallidal neurons. J Neurosci 2003; 23 (05) 1916-1923
- 19 Chiken S, Nambu A. Mechanism of deep brain stimulation: inhibition, excitation, or disruption?. Neuroscientist 2016; 22 (03) 313-322
- 20 Grill WM, Snyder AN, Miocinovic S. Deep brain stimulation creates an informational lesion of the stimulated nucleus. Neuroreport 2004; 15 (07) 1137-1140
- 21 Benabid AL, Pollak P, Louveau A, Henry S, de Rougemont J. Combined (thalamotomy and stimulation) stereotactic surgery of the VIM thalamic nucleus for bilateral Parkinson disease. Appl Neurophysiol 1987; 50 (1-6): 344-346
- 22 Picillo M, Lozano AM, Kou N, Puppi Munhoz R, Fasano A. Programming deep brain stimulation for Parkinson's disease: the Toronto Western Hospital Algorithms. Brain Stimul 2016; 9 (03) 425-437
- 23 Picillo M, Lozano AM, Kou N, Munhoz RP, Fasano A. Programming deep brain stimulation for tremor and dystonia: the Toronto Western Hospital Algorithms. Brain Stimul 2016; 9 (03) 438-452
- 24 Swinnen BEKS, Lotfalla V, Scholten MN. et al. Programming algorithm for the management of speech impairment in subthalamic nucleus deep brain stimulation for Parkinson's disease. Neuromodulation 2024; 27 (03) 528-537
- 25 Kitsios F, Kamariotou M, Syngelakis AI, Talias MA. Recent advances of artificial intelligence in healthcare: a systematic literature review. Appl Sci (Basel) 2023; 13 (13) 7479-7479
- 26 Sutton RS, Barto AG. Reinforcement learning: An introduction. 2nd ed. Cambridge (MA): MIT Press; 2018
- 27 Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science 2006; 313 (5786) 504-507
- 28 Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge (MA): MIT Press; 2016
- 29 Jurafsky D, Martin JH. Speech and language processing. 3rd ed. London: Pearson; 2023
- 30 Vaswani A, Shazeer N, Parmar N. et al. Attention is all you need. In: Advances in neural information processing systems. Red Hook (NY): Curran Associates, Inc.; 2017: 5998-6008
- 31 Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J. , & Houlsby N. An Image is Worth 16 × 16 Words: Transformers for Image Recognition at Scale. ArXiv; 2020 abs/2010.11929.
- 32 Vaswani A, Shazeer N, Parmar N. , et al. ( 2017. Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems, 30 (pp. 5998–6008). Curran Associates, Inc. Accessed November 26, 2025 at: https://arxiv.org/abs/1706.03762
- 33 Gulshan V, Peng L, Coram M. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316 (22) 2402-2410
- 34 Yang JH, Goodman ED, Dawes AJ. et al. Using AI and computer vision to analyze technical proficiency in robotic surgery. Surg Endosc 2023; 37 (04) 3010-3017
- 35 Chan PY, Tay A, Chen D. et al. Ambient intelligence-based monitoring of staff and patient activity in the intensive care unit. Aust Crit Care 2023; 36 (01) 92-98
- 36 Horovistiz A, Oliveira M, Araújo H. Computer vision-based solutions to overcome the limitations of wireless capsule endoscopy. J Med Eng Technol 2023; 47 (04) 242-261
- 37 Artusi CA, Lopiano L, Morgante F. Deep brain stimulation selection criteria for Parkinson's disease: time to go beyond CAPSIT-PD. J Clin Med 2020; 9 (12) 3931
- 38 Mameli F, Zirone E, Girlando R. et al. Role of expectations in clinical outcomes after deep brain stimulation in patients with Parkinson's disease: a systematic review. J Neurol 2023; 270 (11) 5274-5287
- 39 Currie AD, Burke RM, Nunez AEM. et al. The role of a social worker in the deep brain stimulation preoperative evaluation: the DBS-FACTS screening tool. Mov Disord Clin Pract 2025; 12 (07) 974-978
- 40 Sarmento F, Daga A, Wang A. et al. Motor outcomes in unilateral, bilateral rapid, and bilateral delayed staging deep brain stimulation for Parkinson's disease. J Parkinsons Dis 2024; 14 (08) 1614-1622
- 41 Farrokhi F, Buchlak QD, Sikora M. et al. Investigating risk factors and predicting complications in deep brain stimulation surgery with machine learning algorithms. World Neurosurg 2020; 134: e325-e338
- 42 Habets JGV, Janssen MLF, Duits AA. et al. Machine learning prediction of motor response after deep brain stimulation in Parkinson's disease-proof of principle in a retrospective cohort. PeerJ 2020; 8: e10317
- 43 Shang R, He L, Ma X, Ma Y, Li X. Connectome-based model predicts deep brain stimulation outcome in Parkinson's disease. Front Comput Neurosci 2020; 14: 571527
- 44 Haliasos N, Giakoumettis D, Gnanaratnasingham P. et al. Personalizing deep brain stimulation therapy for Parkinson's disease with whole-brain MRI radiomics and machine learning. Cureus 2024; 16 (05) e59915
- 45 Oliveira AL, Coelho M, Guedes LC, Cattoni MB, Carvalho H, Duarte-Batista P. Performance of ChatGPT 3.5 and 4 as a tool for patient support before and after DBS surgery for Parkinson's disease. Neurol Sci 2024; 45 (12) 5757-5764
- 46 Maroufi SF, Fallahi MS, Hosseinzadeh Asli S. et al. Awake versus asleep deep brain stimulation for Parkinson's disease: a comprehensive systematic review and meta-analysis. J Neurosurg 2024; 142 (02) 324-338
- 47 Starr PA, Turner RS, Rau G. et al. Microelectrode-guided implantation of deep brain stimulators into the globus pallidus internus for dystonia: techniques, electrode locations, and outcomes. Neurosurg Focus 2004; 17 (01) E4
- 48 Wong S, Baltuch GH, Jaggi JL, Danish SF. Functional localization and visualization of the subthalamic nucleus from microelectrode recordings acquired during DBS surgery with unsupervised machine learning. J Neural Eng 2009; 6 (02) 026006
- 49 Ciecierski K, Mandat T, Rola R, Raś ZW, Przybyszewski AW. “Computer aided subthalamic nucleus (STN) localization during deep brain stimulation (DBS) surgery in Parkinson's patients.”. In Annales Academiae Medicae Silesiensis, vol. 68, no. 5, pp. 275–283. Śląski Uniwersytet Medyczny w Katowicach, 2014
- 50 Chaovalitwongse W, Jeong Y, Jeong MK, Danish S, Wong S. Pattern recognition approaches for identifying subcortical targets during deep brain stimulation surgery. IEEE Intell Syst 2011; 26 (05) 54-63
- 51 Valsky D, Marmor-Levin O, Deffains M. et al. Stop! border ahead: automatic detection of subthalamic exit during deep brain stimulation surgery. Mov Disord 2017; 32 (01) 70-79
- 52 Yıldırım Ö, Pławiak P, Tan R-S, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 2018; 102: 411-420
- 53 Cohen I, Valsky D, Talmon R. Unsupervised detection of sub-territories of the subthalamic nucleus during DBS surgery with manifold learning. IEEE Trans Biomed Eng 2023; 70 (04) 1286-1297
- 54 Baloglu UB, Talo M, Yildirim O, Ru ST, Rajendra Acharya U. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognit Lett 2019; 122: 23-30
- 55 Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 2018; 100: 270-278
- 56 Hosny M, Zhu M, Gao W, Fu Y. Deep convolutional neural network for the automated detection of subthalamic nucleus using MER signals. J Neurosci Methods 2021; 356: 109145
- 57 Bermudez C, Rodriguez W, Huo Y. , et al. ( 2019. , March). Towards machine learning prediction of deep brain stimulation (DBS) intra-operative efficacy maps. In Medical imaging 2019: image processing (Vol. 10949, pp. 528–534). SPIE.
- 58 Horn A, Reich M, Vorwerk J. et al. Connectivity predicts deep brain stimulation outcome in Parkinson disease. Ann Neurol 2017; 82 (01) 67-78
- 59 Vaillancourt DE, Barmpoutis A, Wu SS. et al; AIDP Study Group. Automated imaging differentiation for Parkinsonism. JAMA Neurol 2025; 82 (05) 495-505
- 60 Liu Y, Xiao B, Zhang C. et al. Predicting motor outcome of subthalamic nucleus deep brain stimulation for Parkinson's disease using quantitative susceptibility mapping and radiomics: a pilot study. Front Neurosci 2021; 15: 731109
- 61 Boutet A, Madhavan R, Elias GJB. et al. Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning. Nat Commun 2021; 12 (01) 3043
- 62 Qiu J, Ajala A, Karigiannis J. et al. Deep learning and fMRI-based pipeline for optimization of deep brain stimulation during Parkinson's disease treatment: toward rapid semi-automated stimulation optimization. IEEE J Transl Eng Health Med 2024; 12: 589-599
- 63 Massano J, Bhatia KP. Deep brain stimulation in Parkinson's disease: cognitive effects and practical considerations. Front Neurol 2012; 3: 114
- 64 Suppa A. et al. Hypokinetic and ataxic speech disturbances after subthalamic nucleus deep brain stimulation. Front Neurol 2023; 14: 1061234
- 65 Chiu SY, Chen HC, Lin CH. Speech changes following globus pallidus interna deep brain stimulation in Parkinson's disease. J Neural Transm (Vienna) 2020; 127 (08) 1095-1106
- 66 Pozzi NG. et al. Gait disturbances in Parkinson's disease patients with deep brain stimulation: neurophysiology and management. Front Hum Neurosci 2022; 16: 806513
- 67 Balachandar A, Hashim Y, Vaou O, Fasano A. Automated sleep detection in movement disorders using deep brain stimulation and machine learning. Mov Disord 2024; 39 (11) 2097-2102
- 68 Cagle JN, de Araujo T, Johnson KA. et al. Chronic intracranial recordings in the globus pallidus reveal circadian rhythms in Parkinson's disease. Nat Commun 2024; 15 (01) 4602
- 69 Suppa A, Asci F, Costantini G. et al; Lazio DBS Study Group. 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
- 70 Geraedts VJ, Koch M, Kuiper R. et al. Preoperative electroencephalography-based machine learning predicts cognitive deterioration after subthalamic deep brain stimulation. Mov Disord 2021; 36 (10) 2324-2334
- 71 Koch M, Wang H, Bäck T. “Machine Learning for Predicting the Damaged Parts of a Low Speed Vehicle Crash,”. 2018 Thirteenth International Conference on Digital Information Management (ICDIM), Berlin, Germany, 2018: 179-184
- 72 Little S, Pogosyan A, Neal S. et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann Neurol 2013; 74 (03) 449-457
- 73 Little S, Beudel M, Zrinzo L. et al. Bilateral adaptive deep brain stimulation is effective in Parkinson's disease. J Neurol Neurosurg Psychiatry 2016; 87 (07) 717-721
- 74 Stanslaski S, Summers RLS, Tonder L. et al; ADAPT-PD Investigators. Sensing data and methodology from the adaptive DBS algorithm for personalized therapy in Parkinson's disease (ADAPT-PD) clinical trial. NPJ Parkinsons Dis 2024; 10 (01) 174
- 75 Castaño-Candamil S, Piroth T, Reinacher P, Sajonz B, Coenen VA, Tangermann M. Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson's disease. Neuroimage Clin 2020; 28: 102376
- 76 He S, Baig F, Mostofi A. et al. Closed-loop deep brain stimulation for essential tremor based on thalamic local field potentials. Mov Disord 2021; 36 (04) 863-873
- 77 Castaño-Candamil S, Ferleger BI, Haddock A. et al. A pilot study on data-driven adaptive deep brain stimulation in chronically implanted essential tremor patients. Front Hum Neurosci 2020; 14: 541625
- 78 Chieng LO, Madhavan K, Wang MY. Deep brain stimulation as a treatment for Parkinson's disease related camptocormia. J Clin Neurosci 2015; 22 (10) 1555-1561
- 79 Shih LC, Vanderhorst VG, Lozano AM, Hamani C, Moro E. Improvement of Pisa syndrome with contralateral pedunculopontine stimulation. Mov Disord 2013; 28 (04) 555-556
- 80 Opri E, Cernera S, Molina R. et al. Chronic embedded cortico-thalamic closed-loop deep brain stimulation for the treatment of essential tremor. Sci Transl Med 2020; 12 (572) eaay7680
- 81 Alagapan S, Choi KS, Heisig S. et al. Cingulate dynamics track depression recovery with deep brain stimulation. Nature 2023; 622 (7981) 130-138
- 82 Nuttin BJ, Gabriels L, van Kuyck K, Cosyns P. Electrical stimulation of the anterior limbs of the internal capsules in patients with severe obsessive-compulsive disorder: anecdotal reports. Neurosurg Clin N Am 2003; 14 (02) 267-274
- 83 Sani S, Jobe K, Smith A, Kordower JH, Bakay RA. Deep brain stimulation for treatment of obesity in rats. J Neurosurg 2007; 107 (04) 809-813
- 84 Vassoler FM, White SL, Hopkins TJ. et al. Deep brain stimulation of the nucleus accumbens shell attenuates cocaine reinstatement through local and antidromic activation. J Neurosci 2013; 33 (36) 14446-14454
- 85 Mayberg HS, Lozano AM, Voon V. et al. Deep brain stimulation for treatment-resistant depression. Neuron 2005; 45 (05) 651-660
- 86 Steigerwald F, Kirsch AD, Kühn AA. et al; DBS study group for dystonia. Evaluation of a programming algorithm for deep brain stimulation in dystonia used in a double-blind, sham-controlled multicenter study. Neurol Res Pract 2019; 1: 25