Pith. sign in

REVIEW 3 major objections 5 minor 99 references

Artificial intelligence in deep brain stimulation for movement disorders: a systematic review and technology readiness assessment

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Across 239 studies, AI for deep brain stimulation clusters at technology-readiness levels 2–4, with no system reaching routine clinical deployment—evidence that the bottleneck is validation, not algorithmic capability.

desk verdict Useful, transparent systematic map of AI-DBS with a validation bottleneck worth taking seriously—but the headline 'system' claim overstates the data, and there's a self-citation that slipped past the protocol. read the letter →

arxiv 2607.26666 v1 pith:XOJ7H3VS submitted 2026-07-29 q-bio.NC

Zohra Souei , Muhammad Mushhood Ur Rehman , Harith Akram , Jocelyne Bloch , Stephan Chabardes , Alfonso Fasano , Marwan Hariz , Joachim K. Krauss
show 9 more authors
Andrea A. Kühn Patricia Limousin Daniel E. Lumsden Eduardo M. Moraud Elena Moro Martin M. Reich Ludvic Zrinzo Xavier Vasques Laura Cif
This is my paper · ORCID
classification q-bio.NC
keywords deepbrainstimulationartificialintelligencetechnologyreadinesslevelmovementdisordersexternalvalidationclinicaltranslationParkinson'sdiseasemodelgeneralization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review asks whether artificial intelligence for deep brain stimulation in movement disorders is converging on clinical deployment or accumulating proof-of-concept work. Analyzing 239 peer-reviewed studies from 2000 to 2025, it finds that 86.2% sit at technology-readiness levels 2–4, only 13.8% reach levels 5–6, and none reach level 7 or above. External validation appears in 5.86% of studies, temporal validation in 2.51%, and prospective evaluation in 6.27%. The authors conclude that the field is constrained less by algorithmic inadequacy than by scarce validation under real-world variability, and they map which workflow stages—targeting, programming, adaptive stimulation—are closest to near-term clinical value.

What carries the argument

The central instrument is the machine-learning-adapted Technology Readiness Level (TRL) scale, a nine-level rubric that scores how close a system is to deployment based on evidence reported in the study—from conceptual work (TRL 1–2) through proof-of-concept on retrospective data (TRL 4), clinician-facing prototypes (TRL 5), prospective evaluation in workflow context (TRL 6), to integrated pilot deployment, routine use, and multicentre adoption or regulatory clearance (TRL 7–9). It is complemented by a strict three-way distinction among external validation (testing on an independent cohort from a different institution), temporal validation (chronological data splitting or longitudinal follow

What would settle it

A registry-based audit that identifies more than a handful of AI-assisted DBS systems with regulatory clearance or routine institutional use (TRL 7–9) whose studies were missed because they do not index AI terms would undercut the claim that no system reaches TRL ≥7. Equally, a single large prospective multicentre external-validation study in adaptive DBS for motor-state control in Parkinson's disease would directly falsify the claim that this subfield has not exceeded TRL 5 in any signal modality.

Watch

Extended reading notes

Core claim

The central discovery is a quantitative picture of translational immaturity: a machine-learning-adapted Technology Readiness Level assessment of all 239 included studies shows that most AI-DBS systems are proof-of-concept (TRL 4 is the modal level, at 65.7%), none have reached integrated pilot deployment, routine use, or regulatory clearance (TRL ≥7), and the small minority reaching TRL 5–6 concentrate in objective assessment and adaptive DBS. The paper argues that this pattern persists not because the algorithms are too weak but because the field has not generated the external, temporal, and prospective evidence needed to show generalizability across centres, devices, and time.

Load-bearing premise

The finding rests on treating absence of reported validation evidence as absence of validation: TRL assignments were made only from what each study explicitly reported, with the lower level conservatively assigned when reporting was insufficient, so the 86% figure could understate true readiness if many teams have validated systems in trials or registries that do not use AI keywords.

Editorial extensions

If this is right

  • If the TRL distribution is accurate, no current AI-DBS system is deployable as a routine clinical tool, and the near-term priority should shift from building new architectures to generating reproducible, externally validated evidence.
  • Externally validated success is concentrated in tasks with anatomical ground truth (contact selection, STN localization), while tasks generalizing biological or behavioural signals degrade across cohorts—implying near-term clinical value is most likely in targeting and programming.
  • Adaptive DBS, despite the largest study volume, has the lowest external validation rate (2.7%) and no motor-state control work above TRL 5, so closed-loop systems still need proof of stability across time and centres before routine use.
  • With only 5.02% of studies referencing regulatory frameworks and 2.51% addressing model lifecycle management, compliance and governance infrastructure will have to be built mostly from scratch as systems approach deployment.
  • Because 26.4% of studies operate in small-sample, high-dimensional settings and accuracy is the dominant reported metric, reported performances are likely optimistic upper bounds; calibration and decision-curve analysis should become minimum requirements for deployment claims.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension would be to run the same TRL rubric against clinical-trial registries and regulatory databases that do not index AI terms; if many systems are already in routine or trial use without AI-related keywords, the reported immaturity is partly an artifact of search scope.
  • If validation is the bottleneck, then funding or reporting mandates that require external or temporal validation for publication could shift the field faster than any new algorithmic technique; one could compare the TRL distribution of studies published after such mandates with earlier work.
  • The paper's logic suggests a simple leading indicator for the field: the ratio of externally validated studies to total studies per workflow stage, tracked over time—a metric that future reviews could adopt as a standard reporting item.
  • The finding that tasks with clear anatomical ground truth transfer across institutions while behavioural-signal tasks degrade suggests a broader principle: AI trustworthiness in surgery may scale with the objectivity of the ground truth, so the first deployable systems will likely be those whose labels are anatomical or electrical rather than clinical-rating-based.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript is a PRISMA-compliant systematic review of 239 peer-reviewed studies (2000–2025) applying AI to deep brain stimulation (DBS) for movement disorders. It maps AI methods, data modalities, validation practices, and translational maturity using a machine-learning-adapted Technology Readiness Level (TRL) rubric. The authors report a field concentrated on Parkinson's disease and subthalamic nucleus targeting, dominated by internal cross-validation, with rare external (5.86%) and temporal (2.51%) validation. A study-level TRL distribution places 86.2% of reports at TRL 2–4 and none at TRL ≥7. The central conclusion is that most AI-DBS systems remain at early-to-intermediate translational stages and that the bottleneck is validation maturity rather than algorithmic capacity.

Significance. If the findings hold, this review provides a valuable, reproducible evidence map and a structured translational assessment of AI in DBS. Strengths include prospective registration (PROSPERO CRD42023488688), independent dual screening/extraction with adjudication, a publicly available full dataset and code on Figshare, a clearly specified ML-adapted TRL rubric anchored in the external MLTRL framework, and explicit acknowledgement of key limitations. The descriptive distributions (external validation 5.86%, temporal validation 2.51%, TRL 2–4 at 86.2% of studies) are useful and falsifiable. However, the central 'systems' interpretation is partly interpretive and is vulnerable to a unit-of-analysis mismatch between study-level TRL ratings and system-level claims, as well as protocol-expansion transparency concerns.

major comments (3)
  1. [Abstract; Results, 'TRL distribution and translational gaps'; Methods, 'Technology readiness level assessment'] The TRL rubric is applied at the level of individual studies ('TRL assignment [sic] were based solely on information explicitly reported in each study'), but the abstract and discussion repeatedly generalize to 'systems' ('most systems remain at early-to-intermediate translational stages'; 'no reviewed system reached routine clinical deployment'). TRL is a property of a technology system, not a publication. If the same AI system is described across multiple publications—for example, a retrospective proof-of-concept (TRL 4), then a clinician-facing prototype (TRL 5), then a prospective evaluation (TRL 6)—rating each study independently assigns TRL 4 to all three and undercounts cumulative system maturity. No clustering or aggregation by system is described. This is load-bearing because the 86.2% TRL 2–4 figure and the 'most systems' claim are based on per-study ratings. The acknowledged l
  2. [Abstract; Discussion, 'With median TRL 3–4...'] The explanatory claim that the field is 'constrained more by limited validation than by algorithmic inadequacy' is an interpretation, not a direct result of the reported analyses. The review documents low rates of external/temporal/prospective validation and notes high internal performance, but it does not systematically compare algorithmic adequacy with validation limitations; there is no analysis of whether low-TRL studies have systematically weaker models, no benchmark of achieved performance against known ceilings, and no evidence that algorithmic capacity is uniform across the corpus. As phrased, this overstates an explanatory conclusion. Please present this explicitly as an interpretation/hypothesis and, if possible, support it with a sensitivity analysis (e.g., correlation of TRL with reported performance, or comparison of performance declines under external versus internal valida
  3. [Methods, 'Data Extraction and Synthesis' and 'Technology readiness level assessment'] The PROSPERO registration is a strength, but the Methods state that the analytical framework was expanded after initial data extraction to incorporate governance, deployment readiness, and generalisation variables. These added variables are precisely the features that feed the TRL and validation conclusions. The assertion that this expansion 'was driven by the exploratory nature of the evidence base rather than by post-hoc outcome considerations' cannot be verified from the manuscript. Please report the protocol amendments explicitly, with dates, and label the corresponding analyses as exploratory in the Results and Discussion. This is a load-bearing transparency issue for a systematic review and should be weighed in the interpretation of the central claim.
minor comments (5)
  1. [Methods, 'Technology readiness level assessment'] Typo: 'TRL assignment were based' should be 'TRL assignments were based'.
  2. [References, ref. 75] Reference 75 is cited as an arXiv preprint dated 2026. If this is a non-peer-reviewed preprint, it should be labeled as such, and the systematic review should not rely on it as supporting evidence for a clinical-utility claim.
  3. [Results, 'Validation landscape'] The phrase 'Dedicated segmentation metrics ... appeared in only 12.9% of targeting-related metric mentions' is ambiguous: clarify whether this is 12.9% of targeting studies or 12.9% of metric mentions.
  4. [Table 3] The 'External validation rate' column entries such as '2/62' would be clearer as percentages, especially because the text reports percentages (e.g., 14.8%, 2.7%). Ensure all workflow stages are represented consistently.
  5. [Figure 4B] The legend states that papers using multiple input feature categories appear in multiple columns, but it is not clear how the TRL dot colors are handled for multi-feature papers; please clarify the counting and coloring rules.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: central TRL conclusion is anchored to an external MLTRL framework and independent validation indicators; self-citations are not load-bearing.

full rationale

The paper's central claim is a synthesis of 239 extracted studies benchmarked against the externally published MLTRL framework (Lavin et al., Nat. Commun. 2022, ref 100), not against any prior result by the same authors. TRL levels were assigned from each study's explicitly reported provenance, validation, and integration evidence (Methods, 'Technology readiness level assessment'), with independent dual-reviewer rating and conservative assignment; the conclusion that validation is the bottleneck is corroborated by separately extracted indicators (external 5.86%, temporal 2.51%, prospective 6.27%), not derived from the paper's own models or fitted parameters. The authors explicitly acknowledge that TRL estimates may be conservative and that the MLTRL framework involves boundary judgements, which further indicates an externally anchored assessment rather than a self-referential derivation. Existing self-citations (e.g., refs 9, 75, 97) are supporting examples or general recommendations and are not load-bearing. The study-level versus system-level TRL interpretation is a validity/generalizability concern, not a circularity: it does not make any conclusion equal to its input by construction.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No free parameters or invented entities. The review depends on domain assumptions about the completeness and accuracy of published reports and the meaningfulness of the adapted TRL scale.

assumptions (5)
  • domain assumption Self-reported validation practices in the 239 included studies correspond to what was actually done
    The review's validation rates (external 5.86%, temporal 2.51%) are extracted from what each paper reports; if papers under-report validation, rates would be underestimated.
  • domain assumption TRL can be assigned from text alone with the adapted rubric
    Methods section says TRL assignments were based solely on information explicitly reported in each study; this assumes published text contains enough evidence to judge deployment maturity.
  • domain assumption English-language journal articles indexed in five databases from 2000-2025 adequately represent the field
    Eligibility criteria restrict to English and peer-reviewed articles; non-English, preprint, or non-AI-indexed clinical deployment literature is excluded.
  • domain assumption Patterns from the PD/STN-dominant corpus extend to other indications and targets
    Authors explicitly caution in the Discussion that their principal findings derive predominantly from STN-targeted PD studies; any broader statement assumes transferability.
  • domain assumption The adapted MLTRL framework is appropriate for clinical AI readiness
    The authors adapted an engineering TRL framework to healthcare AI; validity of that mapping is assumed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Artificial intelligence in deep brain stimulation for movement disorders: a systematic review and technology readiness assessment." pith.science (2026). https://pith.science/paper/XOJ7H3VS

@misc{pith2026260726666,
  author       = {Pith},
  title        = {Pith review of: Artificial intelligence in deep brain stimulation for movement disorders: a systematic review and technology readiness assessment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XOJ7H3VS}},
  note         = {Machine review of arXiv:2607.26666}
}
read the original abstract

Artificial intelligence (AI) is increasingly explored across deep brain stimulation (DBS) for movement disorders, yet whether current systems are approaching deployment remains unclear. To characterise their scope, validation maturity, and translational readiness, we systematically evaluated 239 peer-reviewed studies published between 2000 and 2025, assessing AI methods, validation practices, and barriers constraining clinical translation. Research was dominated by Parkinson's disease and subthalamic nucleus targeting, with limited coverage of other disorders and targets. Most studies reported encouraging internal performance; however, external validation was rare, evaluations remained predominantly retrospective and single-centre, and more than one-quarter involved small-sample, high-dimensional datasets with elevated overfitting risk. Technology readiness assessment revealed that most systems remain at early-to-intermediate translational stages, constrained more by limited validation than by algorithmic inadequacy, compounded by the biological heterogeneity and dynamic complexity inherent to DBS. Nevertheless, emerging external and prospective studies suggest a field moving toward clinical maturity, with promising applications in targeting, programming, outcome prediction, and adaptive therapy delivery.

Figures

Figures reproduced from arXiv: 2607.26666 by the authors.

Figure 1
Figure 1. PRISMA flow diagram [PITH_FULL_IMAGE:figures/full_fig_p038_1.png] view at source ↗
Figure 3
Figure 3. Explainability practices and validation strategies across the DBS workflow (A) XAI adoption by workflow stage. (B) XAI method types by workflow stage; feature importance dominates, post-hoc methods (SHAP, attention) remain marginal. (C) Validation strategy distribution by workflow stage; k-fold cross-validation predominates, temporal validation is negligible. (D) Validation types by workflow stage; internal validati… view at source ↗
Figure 4
Figure 4. Technology readiness level of AI applications across DBS workflow stages (A) Technology readiness level mapped to workflow stage. (N=239) Most stages are dominated by TRL 4 studies. No reviewed system reached routine clinical deployment (TRL ≥7). Systems with prospective clinical evaluation (TRL 6) represented only 5.8% of the full corpus, concentrated in programming (11.1%), aDBS (9.6%), and targeting (6.4%); all r… view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Translational landscape of AI-driven DBS: loop architecture, methodological gaps, and reform pathways (A) Loop maturity continuum in adaptive deep brain stimulation. Four progressive stages from fixed-parameter open-loop DBS to therapeutic BCI, mapped to representative…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

99 extracted references · 1 canonical work pages

  1. [1]

    Kupsch, A. et al. Pallidal deep-brain stimulation in primary generalized or segmental dystonia. N. Engl. J. Med. 355, 1978–1990 (2006)

    1978

  2. [2]

    Krack, P. et al. Five-year follow-up of bilateral stimulation of the subthalamic nucleus in advanced Parkinson’s disease. N. Engl. J. Med. 349, 1925–1934 (2003)

    1925

  3. [3]

    Kremer, N. I. et al. Deep brain stimulation for tremor: update on long-term outcomes, target considerations and future directions. J. Clin. Med. 10, 3468 (2021)

    2021

  4. [4]

    Deuschl, G. et al. European Academy of Neurology/Movement Disorder Society ‐ European Section guideline on the treatment of Parkinson’s disease: I. Invasive therapies. Eur. J. Neurol. 29, 2580–2595 (2022)

    2022

  5. [5]

    Jiang, F. et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2, 230–243 (2017)

    2017

  6. [6]

    & Boetzel, K

    Hell, F., Köglsperger, T. & Boetzel, K. Improving the standard for deep brain stimulation therapy: target structures and feedback signals for adaptive stimulation. Current perspectives and future directions. Cureus 10, e2468 (2018)

    2018

  7. [8]

    & Fuentes, R

    Wojtecki, L., Colosimo, C. & Fuentes, R. Deep brain stimulation for movement disorders – a history of success and challenges to conquer. Front. Integr. Neurosci. 6, 6 (2012)

    2012

  8. [9]

    & Akram, H

    Limousin, P. & Akram, H. AI and deep brain stimulation: what have we learned? Nat. Rev. Neurol. 19, 451–452 (2023)

    2023

Show all 99 references
  1. [10]

    Bledsoe, I. O. & Morrison, M. A. Functional imaging of deep brain stimulation in dystonia: a review. Dystonia 2, 11440 (2023)

    2023

  2. [11]

    McIntyre, C. C. & Hahn, P. J. Network perspectives on the mechanisms of deep brain stimulation. Neurobiol. Dis. 38, 329–337 (2010)

    2010

  3. [12]

    & Mohammadi, A

    Ghasemi, P., Sahraee, T. & Mohammadi, A. Closed- and open-loop deep brain stimulation: Methods, challenges, current and future aspects. J. Biomed. Phys. Eng. 8, 209–216 (2018)

    2018

  4. [13]

    Food and Drug Administration

    U.S. Food and Drug Administration. Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan. https://www.fda.gov/media/145022/download (2021)

    2021

  5. [14]

    High-Level Summary of the Artificial Intelligence Act

    European Commission. High-Level Summary of the Artificial Intelligence Act. https://artificialintelligenceact.eu/high-level-summary/ (2023)

    2023

  6. [15]

    & Baxter, J

    Peralta, M., Jannin, P. & Baxter, J. S. H. Machine learning in deep brain stimulation: A systematic review. Artif. Intell. Med. 122, 102198 (2021)

    2021

  7. [16]

    S., Pomeraniec, I

    Chandrabhatla, A. S., Pomeraniec, I. J., Horgan, T. M., Wat, E. K. & Ksendzovsky, A. Landscape and future directions of machine learning applications in closed-loop brain stimulation. npj Digit. Med. 6, 79 (2023)

    2023

  8. [17]

    & Mammis, A

    Lampros, M., Symeou, S., Alexiou, G., Voulgaris, S. & Mammis, A. Applications of machine learning in deep brain stimulation for major depressive disorder: a systematic review and meta-analysis. Neurosurg. Rev. 48, 680 (2025)

    2025

  9. [18]

    M., Gates, S., Garcia, L

    Young, V. M., Gates, S., Garcia, L. Y. & Salardini, A. Data leakage in deep learning for Alzheimer’s disease diagnosis: a scoping review of methodological rigor and performance inflation. Diagnostics 15, 2348 (2025)

    2025

  10. [19]

    A., Brown, P., Gimeno, H., Lin, J.-P

    Shah, S. A., Brown, P., Gimeno, H., Lin, J.-P. & McClelland, V. M. Application of machine learning using decision trees for prognosis of deep brain stimulation of globus pallidus internus for children with dystonia. Front. Neurol. 11, 825 (2020)

    2020

  11. [20]

    Wu, Y. et al. Prediction of subthalamic stimulation efficacy on isolated dystonia via support vector regression. Heliyon 10, e31475 (2024)

    2024

  12. [21]

    Kübler, D. et al. Nucleus basalis of Meynert predicts cognition after deep brain stimulation in Parkinson’s disease. Parkinsonism Relat. Disord. 94, 89–95 (2022)

    2022

  13. [22]

    & Gharabaghi, A

    Ferrea, E., Negahbani, F., Cebi, I., Weiss, D. & Gharabaghi, A. Machine learning explains response variability of deep brain stimulation on Parkinson’s disease quality of life. npj Digit. Med. 7, 269 (2024)

    2024

  14. [23]

    Chang, B. et al. Comprehensive clinical scale-based machine learning model for predicting subthalamic nucleus deep brain stimulation outcomes in Parkinson’s disease. Neurosurg. Rev. 48, 266 (2025)

    2025

  15. [24]

    Hosny, M., Zhu, M., Gao, W. & Fu, Y. A novel deep learning model for STN localization from LFPs in Parkinson’s disease. Biomed. Signal Process. Control 77, 103830 (2022)

    2022

  16. [25]

    Hosny, M., Zhu, M., Su, Y., Gao, W. & Fu, Y. A novel deep recurrent convolutional neural network for subthalamic nucleus localization using local field potential signals. Biocybern. Biomed. Eng. 41, 1561–1574 (2021)

    2021

  17. [26]

    Coelli, S. et al. An intra-operative feature-based classification of microelectrode recordings to support the subthalamic nucleus functional identification during deep brain stimulation surgery. J. Neural Eng. 18, 016003 (2021)

    2021

  18. [27]

    F., Gilmore, G., Jog, M

    Khosravi, M., Atashzar, S. F., Gilmore, G., Jog, M. S. & Patel, R. V. Intraoperative localization of STN during DBS Surgery using a data-driven model. IEEE J. Transl. Eng. Health Med. 8, 1–9 (2020)

    2020

  19. [28]

    & Talmon, R

    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. 70, 1286–1297 (2023)

    2023

  20. [29]

    Rao, A. T. et al. Clinically-derived oscillatory biomarker predicts optimal subthalamic stimulation for Parkinson’s disease. J. Neural Eng. 19, 026013 (2022)

    2022

  21. [30]

    Hosny, M. et al. A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease. Soft Comput. 29, 2343–2362 (2025)

    2025

  22. [31]

    Valsky, D. et al. Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery: detection of STN exit during DBS surgery. Mov. Disord. 32, 70– 79 (2017)

    2017

  23. [32]

    H., Jaggi, J

    Wong, S., Baltuch, G. H., Jaggi, J. L. & Danish, S. F. Functional localization and visualization of the subthalamic nucleus from microelectrode recordings acquired during DBS surgery with unsupervised machine learning. J. Neural Eng. 6, 026006 (2009)

    2009

  24. [33]

    Cao, L. et al. Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection. J. Neural Eng. 16, 066015 (2019)

    2019

  25. [34]

    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 356, 109145 (2021)

    2021

  26. [35]

    Solomon, O. et al. Deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 Tesla MRI. Hum. Brain Mapp. 42, 2862–2879 (2021)

    2021

  27. [36]

    Baumgarten, C. et al. Improvement of pyramidal tract side effect prediction using a data- driven method in subthalamic stimulation. IEEE Trans. Biomed. Eng. 64, 2134–2141 (2017)

    2017

  28. [37]

    Zrinzo, L. et al. Stereotactic localization of the human pedunculopontine nucleus: atlas-based coordinates and validation of a magnetic resonance imaging protocol for direct localization. Brain 131, 1588–1598 (2008)

    2008

  29. [38]

    Li, J. et al. Direct localization and delineation of human pedunculopontine nucleus based on a self‐supervised magnetic resonance image super‐resolution method. Hum. Brain Mapp. 44, 3781–3794 (2023)

    2023

  30. [39]

    Xu, J. et al. Progressive thalamic nuclear atrophy in blepharospasm and blepharospasm- oromandibular dystonia. Brain Commun. 6, fcae117 (2024)

    2024

  31. [40]

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

    2023

  32. [41]

    Boutet, A. et al. Sign-specific stimulation ‘hot’ and ‘cold’ spots in Parkinson’s disease validated with machine learning. Brain Commun. 3, fcab027 (2021)

    2021

  33. [42]

    Lange, F. et al. Machine versus physician-based programming of deep brain stimulation in isolated dystonia: A feasibility study. Brain Stimulat. 16, 1105–1111 (2023)

    2023

  34. [43]

    Haddock, A. et al. Automated deep brain stimulation programming for tremor. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 1618–1625 (2018)

    2018

  35. [44]

    Sarikhani, P. et al. Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson’s disease and essential tremor. J. Neural Eng. 19, 046042 (2022)

    2022

  36. [45]

    Qiu, 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. 12, 589–599 (2024)

    2024

  37. [46]

    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)

    2021

  38. [47]

    N., Osting, B., Vorwerk, J., Dorval, A

    Anderson, D. N., Osting, B., Vorwerk, J., Dorval, A. D. & Butson, C. R. Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes. J. Neural Eng. 15, 026005 (2018)

    2018

  39. [48]

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

    2022

  40. [49]

    Su, L. et al. A novel fuzzy reinforcement learning approach for personalization deep brain stimulation with communication delay. Biomed. Signal Process. Control 106, 107736 (2025)

    2025

  41. [50]

    Connolly, M. J. et al. Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson’s disease. J. Neural Eng. 18, 046046 (2021)

    2021

  42. [51]

    & Brown, P

    Loukas, C. & Brown, P. A PC-based system for predicting movement from deep brain signals in Parkinson’s disease. Comput. Methods Programs Biomed. 107, 36–44 (2012)

    2012

  43. [52]

    He, S. et al. Closed-loop DBS triggered by real-time movement and tremor decoding based on thalamic LFPs for essential tremor. in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 3602–3605 (IEEE, Montreal, QC, Canada, 2020)...

    2020

  44. [53]

    Camara, C. et al. Non-linear dynamical analysis of resting tremor for demand-driven deep brain stimulation. Sensors 19, 2507 (2019)

    2019

  45. [54]

    Lee, K. et al. Real-time decoding of leg motor function and dysfunction from the subthalamic nucleus in people with Parkinson’s disease. in Brain-Computer Interface Research (eds Guger, C., Allison, B., Rutkowski, T. M. & Korostenskaja, M.) 83–92 (Springer Nature Switzerland, ...

    2024 doi

  46. [55]

    Dixon, T. C. et al. Movement-responsive deep brain stimulation for Parkinson’s disease using a remotely optimized neural decoder. Nat. Biomed. Eng. 10, 110–124 (2025)

    2025

  47. [56]

    M., Hebb, A

    Golshan, H. M., Hebb, A. O. & Mahoor, M. H. LFP-Net: A deep learning framework to recognize human behavioral activities using brain STN-LFP signals. J. Neurosci. Methods 335, 108621 (2020)

    2020

  48. [57]

    Yin, Z. et al. Pallidal activities during sleep and sleep decoding in dystonia, Huntington’s, and Parkinson’s disease. Neurobiol. Dis. 182, 106143 (2023)

    2023

  49. [58]

    & Slavin, K

    Shukla, P., Basu, I., Graupe, D., Tuninetti, D. & Slavin, K. V. A neural network-based design of an on-off adaptive control for Deep Brain Stimulation in movement disorders. in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 4140–41...

    2012

  50. [59]

    Castaño-Candamil, S. et al. A pilot study on data-driven adaptive deep brain stimulation in chronically implanted essential tremor patients. Front. Hum. Neurosci. 14, 541625 (2020)

    2020

  51. [60]

    C., Thompson, M

    Houston, B. C., Thompson, M. C., Ojemann, J. G., Ko, A. L. & Chizeck, H. J. Classifier- based closed-loop deep brain stimulation for essential tremor. in 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) 316–320 (IEEE, Shanghai, China, 2017). doi:10.1109/...

    2017

  52. [61]

    Haddock, A., Chizeck, H. J. & Ko, A. L. Deep neural networks for context-dependent deep brain stimulation. in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) 957–960 (IEEE, San Francisco, CA, USA, 2019). doi:10.1109/NER.2019.8717056

    2019

  53. [62]

    & Chizeck, H

    Houston, B., Thompson, M., Ko, A. & Chizeck, H. A machine-learning approach to volitional control of a closed-loop deep brain stimulation system. J. Neural Eng. 16, 016004 (2019)

    2019

  54. [63]

    & Tuninetti, D

    Khobragade, N., Graupe, D. & Tuninetti, D. Towards fully automated closed-loop Deep Brain Stimulation in Parkinson’s disease patients: a LAMSTAR-based tremor predictor. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ...

    2015

  55. [64]

    Kuhner, A. et al. Correlations between motor symptoms across different motor tasks, quantified via random forest feature classification in Parkinson’s disease. Front. Neurol. 8, 607 (2017)

    2017

  56. [65]

    & Demosthenous, A

    Mohammed, A., Zamani, M., Bayford, R. & Demosthenous, A. Patient specific Parkinson’s disease detection for adaptive deep brain stimulation. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 1528–1531 (IEEE, Milan, 2015...

    2015

  57. [66]

    Tan, H. et al. Decoding voluntary movements and postural tremor based on thalamic LFPs as a basis for closed-loop stimulation for essential tremor. Brain Stimulat. 12, 858–867 (2019)

    2019

  58. [67]

    & Hirschmann, J

    Todorov, D., Schnitzler, A. & Hirschmann, J. Parkinsonian rest tremor can be distinguished from voluntary hand movements based on subthalamic and cortical activity. Clin. Neurophysiol. 157, 146–155 (2024)

    2024

  59. [68]

    Cole, E. R. & Miocinovic, S. Are we ready for automated deep brain stimulation programming? Parkinsonism Relat. Disord. 134, 107347 (2025)

    2025

  60. [69]

    & Vaidyanathan, R

    Angeles, P., Tai, Y., Pavese, N., Wilson, S. & Vaidyanathan, R. Automated assessment of symptom severity changes during deep brain stimulation (DBS) therapy for Parkinson’s disease. in 2017 International Conference on Rehabilitation Robotics (ICORR) 1512–1517 (IEEE, London, 20...

    2017

  61. [70]

    Castano-Candamil, S. et al. An easy-to-use and fast assessment of patient-specific dbs- induced changes in hand motor control in Parkinson’s disease. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 2155–2163 (2019)

    2019

  62. [71]

    & Hadjileontiadis, L

    Ziogas, I., Lamprou, C. & Hadjileontiadis, L. J. Bispectral analysis of Parkinsonian rest tremor: new characterization and classification insights pre-/post-dbs and medication treatment. IEEE Access 11, 114459–114474 (2023)

    2023

  63. [72]

    Friedrich, M. U. et al. Validation and application of computer vision algorithms for video- based tremor analysis. npj Digit. Med. 7, 165 (2024)

    2024

  64. [73]

    Peach, R. et al. Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning. npj Digit. Med. 7, 160 (2024)

    2024

  65. [74]

    Sanderson, J. B. et al. Multi-dimensional, short-timescale quantification of parkinson’s disease and essential tremor motor dysfunction. Front. Neurol. 11, 886 (2020)

    2020

  66. [75]

    Cif, L. et al. Deep learning pose estimation for multi-label recognition of combined hyperkinetic movement disorders. arXiv https://doi.org/10.48550/ARXIV.2602.00163 (2026)

    2026 doi arXiv

  67. [76]

    Sedov, A. et al. Implications of asymmetric neural activity patterns in the basal ganglia outflow in the integrative neural network model for cervical dystonia. in Progress in Brain Research vol. 249 261–268 (Elsevier, 2019)

    2019

  68. [77]

    De A Marcelino, A. L. et al. Pallidal neuromodulation of the explore/exploit trade-off in decision-making. eLife 12, e79642 (2023)

    2023

  69. [78]

    Chu, C. et al. Subthalamic stimulation modulates motor network in Parkinson’s disease: recover, relieve and remodel. Brain 146, 2780–2791 (2023)

    2023

  70. [79]

    Geng, X. et al. Subthalamic and pallidal oscillations and their couplings reflect dystonia severity and improvements by deep brain stimulation. Neurobiol. Dis. 199, 106581 (2024)

    2024

  71. [80]

    Fougère, M. et al. Optogenetic stimulation of glutamatergic neurons in the cuneiform nucleus controls locomotion in a mouse model of Parkinson’s disease. Proc. Natl. Acad. Sci. 118, e2110934118 (2021)

    2021

  72. [81]

    Cho, N. et al. Hypothalamic deep brain stimulation augments walking after spinal cord injury. Nat. Med. 30, 3676–3686 (2024)

    2024

  73. [82]

    Kaymak, A. et al. Spiking patterns in the globus pallidus highlight convergent neural dynamics across diverse genetic dystonia syndromes. Ann. Neurol. 97, 826–844 (2025)

    2025

  74. [83]

    Kim, J. et al. Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: application in deep brain stimulation. Hum. Brain Mapp. 40, 679–698 (2019)

    2019

  75. [84]

    D., Álvarez, M

    Vargas Cardona, H. D., Álvarez, M. A. & Orozco, Á. A. Multi-task learning for subthalamic nucleus identification in deep brain stimulation. Int. J. Mach. Learn. Cybern. 9, 1181–1192 (2018)

    2018

  76. [85]

    P., Courtine, G

    Lacour, S. P., Courtine, G. & Guck, J. Materials and technologies for soft implantable neuroprostheses. Nat. Rev. Mater. 1, 16063 (2016)

    2016

  77. [86]

    Ruge, D. et al. Shaping reversibility? Long-term deep brain stimulation in dystonia: the relationship between effects on electrophysiology and clinical symptoms. Brain 134, 2106– 2115 (2011)

    2011

  78. [87]

    & Fasano, A

    Balachandar, A., Hashim, Y., Vaou, O. & Fasano, A. Automated sleep detection in movement disorders using deep brain stimulation and machine learning. Mov. Disord. 39, 2097–2102 (2024)

  79. [88]

    Chen, Y. et al. Automatic sleep stage classification based on subthalamic local field potentials. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 118–128 (2019)

    2019

  80. [89]

    Gong, C., Chen, Y., Sui, Y. & Li, L. Automatic sleep stage classification with cross-modal self-supervised features from deep brain signals. in 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) 1–4 (IEEE, Baltimore, MD, USA, 2023). doi:10.1109/NER52421.2...

    2023

  81. [90]

    W., Cui, C., Wilkins, K

    Choi, J. W., Cui, C., Wilkins, K. B. & Bronte-Stewart, H. M. N2GNet tracks gait performance from subthalamic neural signals in Parkinson’s disease. npj Digit. Med. 8, 7 (2025)

    2025

  82. [91]

    & Graupe, D

    Khobragade, N., Tuninetti, D. & Graupe, D. On the need for adaptive learning in on-demand deep brain stimulation for movement disorders. in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2190–2193 (IEEE, Honolulu, HI, U...

    2018

  83. [92]

    Reinke, A. et al. Understanding metric-related pitfalls in image analysis validation. Nat. Methods 21, 182–194 (2024)

    2024

  84. [93]

    Van Calster, B. et al. Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance. Lancet Digit. Health 7, 100916 (2025)

    2025

  85. [94]

    Discovering themes in deep brain stimulation research using explainable artificial intelligence

    Allen, B. Discovering themes in deep brain stimulation research using explainable artificial intelligence. Biomedicines 11, 771 (2023)

    2023

  86. [95]

    Lekadir, K. et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 388, e081554 (2025)

    2025

  87. [96]

    Kannan, M. et al. An enhancement of machine learning model performance in disease prediction with synthetic data generation. Sci. Rep. 15, 33482 (2025)

    2025

  88. [97]

    & Cif, L

    Vasques, X. & Cif, L. Synthetic data generation for classifying electrophysiological and morpho-electrophysiological neurons from mouse visual cortex. Neuroinformatics 24, 2 (2025)

    2025

  89. [98]

    Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71 (2021)

    2020

  90. [99]

    Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement

    PRISMA-P Group et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 4, 1 (2015)

    2015

  91. [100]

    black box

    Lavin, A. et al. Technology readiness levels for machine learning systems. Nat. Commun. 13, 6039 (2022). Figure legends Figure 1: PRISMA flow diagram Figure 2: Landscape of AI application across DBS stages (A) Distribution of movement disorder indications across the 239 includ...

    2022

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.