Evidence map›Paper›PMID 39143345›Full record

SynthesisJournal of neurology2024

Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.

Lazzaro di Biase, Pasquale Maria Pecoraro, Giovanni Pecoraro, Syed Ahmar Shah, Vincenzo Di Lazzaro

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Journal of neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Article
  7. Review
  8. Review
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Unlocking autism's complexity: theFrontiers in integrative neuroscience · 2024
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lazzaro di BiaseResearch Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy. l.dibiase@policlinicocampus.it.ORCID http://orcid.org/0000-0001-8753-757X
Pasquale Maria PecoraroResearch Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy.
Giovanni PecoraroTelecommunications Engineer, Rome, Italy.
Syed Ahmar ShahUsher Institute, The University of Edinburgh, Edinburgh, UK.
Vincenzo Di LazzaroResearch Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson's disease diagnosis.

methodsFollowing Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between December, 2007 and July, 2023, using a search string combining "Parkinson's disease" AND ("healthy" or "control") AND "diagnosis", within the Groups and Outcome domains. Additional search terms included "Algorithm", "Technology" and "Performance".

resultsFrom 89 identified studies, 47 met the inclusion criteria based on the search string and four additional studies were included based on the Authors' expertise. Gait emerged as the most common parameter analysed by machine learning models, with Support Vector Machines as the prevalent algorithm. The results suggest promising accuracy with complex algorithms like Random Forest, Support Vector Machines, and K-Nearest Neighbours. DISCUSSION: Despite the promise shown by machine learning algorithms, real-world applications may still face limitations. This review suggests that integrating machine learning with wearable sensors has the potential to improve Parkinson's disease diagnosis. These tools could provide clinicians with objective data, potentially aiding in earlier detection.

Indexed as

Machine LearningParkinson DiseaseWearable Electronic DevicesHumansMachine learningParkinson’s disease (PD)Systematic reviewWearable sensors

Identifiers

PMID39143345

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.