Predictive factors for degenerative lumbar spinal stenosis: a model obtained from a machine learning algorithm technique.
Computer Tomography
Degenerative lumbar spinal stenosis
Machine learning
Spine dimensions
Journal
BMC musculoskeletal disorders
ISSN: 1471-2474
Titre abrégé: BMC Musculoskelet Disord
Pays: England
ID NLM: 100968565
Informations de publication
Date de publication:
23 Mar 2023
23 Mar 2023
Historique:
received:
05
12
2022
accepted:
16
03
2023
entrez:
23
3
2023
pubmed:
24
3
2023
medline:
25
3
2023
Statut:
epublish
Résumé
Degenerative lumbar spinal stenosis (DLSS) is the most common spine disease in the elderly population. It is usually associated with lumbar spine joints/or ligaments degeneration. Machine learning technique is an exclusive method for handling big data analysis; however, the development of this method for spine pathology is rare. This study aims to detect the essential variables that predict the development of symptomatic DLSS using the random forest of machine learning (ML) algorithms technique. A retrospective study with two groups of individuals. The first included 165 with symptomatic DLSS (sex ratio 80 M/85F), and the second included 180 individuals from the general population (sex ratio: 90 M/90F) without lumbar spinal stenosis symptoms. Lumbar spine measurements such as vertebral or spinal canal diameters from L1 to S1 were conducted on computerized tomography (CT) images. Demographic and health data of all the participants (e.g., body mass index and diabetes mellitus) were also recorded. The decision tree model of ML demonstrate that the anteroposterior diameter of the bony canal at L5 (males) and L4 (females) levels have the greatest stimulus for symptomatic DLSS (scores of 1 and 0.938). In addition, combination of these variables with other lumbar spine features is mandatory for developing the DLSS. Our results indicate that combination of lumbar spine characteristics such as bony canal and vertebral body dimensions rather than the presence of a sole variable is highly associated with symptomatic DLSS onset.
Sections du résumé
BACKGROUND
BACKGROUND
Degenerative lumbar spinal stenosis (DLSS) is the most common spine disease in the elderly population. It is usually associated with lumbar spine joints/or ligaments degeneration. Machine learning technique is an exclusive method for handling big data analysis; however, the development of this method for spine pathology is rare. This study aims to detect the essential variables that predict the development of symptomatic DLSS using the random forest of machine learning (ML) algorithms technique.
METHODS
METHODS
A retrospective study with two groups of individuals. The first included 165 with symptomatic DLSS (sex ratio 80 M/85F), and the second included 180 individuals from the general population (sex ratio: 90 M/90F) without lumbar spinal stenosis symptoms. Lumbar spine measurements such as vertebral or spinal canal diameters from L1 to S1 were conducted on computerized tomography (CT) images. Demographic and health data of all the participants (e.g., body mass index and diabetes mellitus) were also recorded.
RESULTS
RESULTS
The decision tree model of ML demonstrate that the anteroposterior diameter of the bony canal at L5 (males) and L4 (females) levels have the greatest stimulus for symptomatic DLSS (scores of 1 and 0.938). In addition, combination of these variables with other lumbar spine features is mandatory for developing the DLSS.
CONCLUSIONS
CONCLUSIONS
Our results indicate that combination of lumbar spine characteristics such as bony canal and vertebral body dimensions rather than the presence of a sole variable is highly associated with symptomatic DLSS onset.
Identifiants
pubmed: 36949452
doi: 10.1186/s12891-023-06330-z
pii: 10.1186/s12891-023-06330-z
pmc: PMC10035245
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
218Subventions
Organisme : The Dan David Foundation, the Tassia and Dr. Joseph Meychan Chair of History and Philosophy of Medicine and the Israel Science Foundation supported this research
ID : (ISF: 1397/08).
Organisme : The Dan David Foundation, the Tassia and Dr. Joseph Meychan Chair of History and Philosophy of Medicine and the Israel Science Foundation supported this research
ID : (ISF: 1397/08).
Informations de copyright
© 2023. The Author(s).
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