Bilwaj Gaonkar 1 , Joel Beckett 1 , Diane Villaroman 1 , Christine Ahn 1 , Matthew Edwards 1 , Steven Moran 1 , Mark Attiah 1 , Diana Babayan 1 , Christopher Ames 1 , J Pablo Villablanca 1 , Noriko Salamon 1 , Alex Bui 1 , Luke Macyszyn 1 Show Affiliations »
Abstract
PURPOSE: To 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 METHODS: By 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. RESULTS: Neural foraminal areas correlated directly with patient height and inversely with patient age. The associations involved were statistically significant (P < .01). CONCLUSION: By 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, 2019Supplemental material is available for this article. 2019 by the Radiological Society of North America, Inc.
PURPOSE: To 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 METHODS: By 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. RESULTS: Neural foraminal areas correlated directly with patient height and inversely with patient age. The associations involved were statistically significant (P < .01). CONCLUSION: By 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, 2019Supplemental material is available for this article. 2019 by the Radiological Society of North America, Inc.
Entities: Chemical
Year: 2019
PMID： 33937788 PMCID： PMC8017393 DOI： 10.1148/ryai.2019180037
Source DB: PubMed Journal: Radiol Artif Intell ISSN： 2638-6100