Machine Learning in Spinal Cord Stimulation for Chronic Pain.
Journal
Operative neurosurgery (Hagerstown, Md.)
ISSN: 2332-4260
Titre abrégé: Oper Neurosurg (Hagerstown)
Pays: United States
ID NLM: 101635417
Informations de publication
Date de publication:
01 08 2023
01 08 2023
Historique:
received:
11
04
2023
accepted:
17
04
2023
pmc-release:
22
05
2024
medline:
19
7
2023
pubmed:
23
5
2023
entrez:
23
5
2023
Statut:
ppublish
Résumé
Spinal cord stimulation (SCS) is an effective treatment for chronic neuropathic pain. The success of SCS is dependent on candidate selection, response to trialing, and programming optimization. Owing to the subjective nature of these variables, machine learning (ML) offers a powerful tool to augment these processes. Here we explore what work has been done using data analytics and applications of ML in SCS. In addition, we discuss aspects of SCS which have narrowly been influenced by ML and propose the need for further exploration. ML has demonstrated a potential to complement SCS to an extent ranging from assistance with candidate selection to replacing invasive and costly aspects of the surgery. The clinical application of ML in SCS shows promise for improving patient outcomes, reducing costs of treatment, limiting invasiveness, and resulting in a better quality of life for the patient.
Identifiants
pubmed: 37219574
doi: 10.1227/ons.0000000000000774
pii: 01787389-202308000-00002
pmc: PMC10586864
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
112-116Subventions
Organisme : NCI NIH HHS
ID : R01 CA166379
Pays : United States
Organisme : NIBIB NIH HHS
ID : U54 EB015408
Pays : United States
Organisme : NINDS NIH HHS
ID : U44 NS115111
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB030324
Pays : United States
Informations de copyright
Copyright © Congress of Neurological Surgeons 2023. All rights reserved.
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