Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Aug 6, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

160

Predictive factors for degenerative lumbar spinal stenosis: a model obtained from a machine learning algorithm

Janan Abbas1,2, Malik Yousef3, Natan Peled4

  • 1Department of Physical Therapy, Zefat Academic College, 13206, Zefat, Israel. Janan1705@gmail.com.

BMC Musculoskeletal Disorders
|March 23, 2023
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A taphonomic reassessment of Qafzeh 25 and its implications for violence, health and funerary practices.

Scientific reports·2026
Same author

Evolutionary Specializations of the Human Vertebral Body and Intervertebral Disc in Relation to Bipedalism.

Life (Basel, Switzerland)·2026
Same author

Low Back Pain Characteristics Among Health Science Undergraduates: A Prospective Study for 2-Year Follow Up.

Journal of clinical medicine·2026
Same author

Correction: Engineering novel features for diabetes complication prediction using synthetic electronic health records.

Frontiers in genetics·2025
Same author

Association of antegonial notch size with craniofacial morphology and masticatory muscle dimensions.

Scientific reports·2025
Same author

3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

PloS one·2025
This summary is machine-generated.

Machine learning identified key lumbar spine measurements predicting degenerative lumbar spinal stenosis (DLSS). Bony canal and vertebral dimensions, not single factors, are crucial for DLSS development in elderly patients.

Area of Science:

  • Spine Surgery
  • Radiology
  • Medical Informatics

Background:

  • Degenerative lumbar spinal stenosis (DLSS) is a prevalent spine condition in the elderly, often linked to degeneration of lumbar spine joints or ligaments.
  • Machine learning (ML) applications in spine pathology are emerging, offering advanced big data analysis capabilities.
  • This study addresses the rarity of ML in spine pathology by investigating its use in predicting symptomatic DLSS.

Purpose of the Study:

  • To identify essential variables predicting the development of symptomatic DLSS.
  • To utilize the random forest ML algorithm for analyzing spine pathology data.
  • To enhance understanding of DLSS etiology through data-driven insights.

Main Methods:

  • A retrospective study comparing 165 patients with symptomatic DLSS to 180 asymptomatic individuals.
Keywords:
Computer TomographyDegenerative lumbar spinal stenosisMachine learningSpine dimensions

More Related Videos

A Mouse Model of Lumbar Spine Instability
05:28

A Mouse Model of Lumbar Spine Instability

Published on: April 23, 2021

8.0K
3D Printing Model of a Patient's Specific Lumbar Vertebra
07:30

3D Printing Model of a Patient's Specific Lumbar Vertebra

Published on: April 14, 2023

2.0K

Related Experiment Videos

Last Updated: Aug 6, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

160
A Mouse Model of Lumbar Spine Instability
05:28

A Mouse Model of Lumbar Spine Instability

Published on: April 23, 2021

8.0K
3D Printing Model of a Patient's Specific Lumbar Vertebra
07:30

3D Printing Model of a Patient's Specific Lumbar Vertebra

Published on: April 14, 2023

2.0K
  • Analysis of lumbar spine measurements (vertebral and spinal canal diameters L1-S1) from CT images.
  • Inclusion of demographic and health data (e.g., BMI, diabetes mellitus) for comprehensive analysis.

    • Main Results:

    • The ML decision tree model highlighted the anteroposterior diameter of the bony canal at L5 (males) and L4 (females) as significant predictors.
    • These key variables demonstrated high stimulus scores (1 for L5 males, 0.938 for L4 females) for symptomatic DLSS.
    • The combination of these and other lumbar spine features is essential for DLSS development.

    Conclusions:

    • Lumbar spine characteristics, including bony canal and vertebral body dimensions, are strongly associated with symptomatic DLSS onset.
    • The development of symptomatic DLSS is linked to a combination of factors rather than a single variable.
    • ML techniques can effectively identify critical predictors in complex spine conditions like DLSS.