ArticleRadiology. Artificial intelligence2019
Quantitative Analysis of Neural Foramina in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.
Article in Radiology. Artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.Neurosurgical review · 2022Pooled it
- A CT-based radiographic analysis of parameters of congenital lumbar neuroforaminal stenosis.Journal of spine surgery (Hong Kong) · 2025Article
- Facet joint tropism in degenerative lumbar scoliosis: a retrospective case-control study.Spine deformity · 2025Article
- Anatomic Assessment of L1-S1 Neuroforaminal Dimensions Using Computed Tomography in 1,000 Patients: A Follow-Up Study.Global spine journal · 2025Article
- Applications of Artificial Intelligence and Machine Learning in Spine MRI.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Establishment of Age- and Sex-Specific Reference Cerebral Ventricle Volumes.World neurosurgery · 2023Article
- Semi-automatic quantitative analysis of the pelvic bony structures on apparent diffusion coefficient maps based on deep learning: establishment of reference ranges.Quantitative imaging in medicine and surgery · 2022Article
- Fully automated pelvic bone segmentation in multiparameteric MRI using a 3D convolutional neural network.Insights into imaging · 2021Article
- Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2021Article
- Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation.Medical image analysis · 2021Article
- Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.AJNR. American journal of neuroradiology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo use machine learning tools and leverage big data informatics to statistically model the variation in the area of lumbar neural foramina in a large asymptomatic population. MATERIALS AND
methodsBy using an electronic health record and imaging archive, lumbar MRI studies in 645 male (mean age, 50.07 years) and 511 female (mean age, 48.23 years) patients between 20 and 80 years old were identified. Machine learning algorithms were used to delineate lumbar neural foramina autonomously and measure their areas. The relationship between neural foraminal area and patient age, sex, and height was studied by using multivariable linear regression.
resultsNeural foraminal areas correlated directly with patient height and inversely with patient age. The associations involved were statistically significant (
conclusionBy using machine learning and big data techniques, a linear model encoding variation in lumbar neural foraminal areas in asymptomatic individuals has been established. This model can be used to make quantitative assessments of neural foraminal areas in patients by comparing them to the age-, sex-, and height-adjusted population averages.© RSNA, 2019
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Registered trials
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.