Definition of Normal Vertebral Morphometry Using NHANES‐II Radiographs

Abstract

A robust definition of normal vertebral morphometry is required to confidently identify abnormalities such as fractures. The Second National Health and Nutrition Examination Survey (NHANES-II) collected a nationwide probability sample to document the health status of the United States. Over 10,000 lateral cervical spine and 7,000 lateral lumbar spine X-rays were collected. Demographic, anthropometric, health, and medical history data were also collected. The coordinates of the vertebral body corners were obtained for each lumbar and cervical vertebra using previously validated, automated technology consisting of a pipeline of neural networks and coded logic. These landmarks were used to calculate six vertebral body morphometry metrics. Descriptive statistics were generated and used to identify and trim outliers from the data. Descriptive statistics were tabulated using the trimmed data for use in quantifying deviation from average for each metric. The dependency of these metrics on sex, age, race, nation of origin, height, weight, and body mass index (BMI) was also assessed. There was low variation in vertebral morphometry after accounting for vertebrae (eg, L1, L2), and the R 2 was high for ANOVAs. Excluding outliers, age, sex, race, nation of origin, height, weight, and BMI were statistically significant for most of the variables, though the F-statistic was very small compared to that for vertebral level. Excluding all variables except vertebra changed the ANOVA R 2 very little. Reference data were generated that could be used to produce standardized metrics in units of SD from mean. This allows for easy identification of abnormalities resulting from vertebral fractures, atypical vertebral body morphometries, and other congenital or degenerative conditions. Standardized metrics also remove the effect of vertebral level, facilitating easy interpretation and enabling data for all vertebrae to be pooled in research studies.

Development and performance verification of AI-based software for quantitative diagnosis of human vertebral fractures

“…The reference range for this threshold is set based on research results for various races and ages [10,12,13,31]. However, in recent years, several studies have reported that vertebral body morphology differs based on race, [32][33][34] gender, [33] and age. [34] Additionally, several studies have shown slight vertebral body crushing even in relatively young people [21,35]; we believe that it is necessary to understand the reference range of vertebral body morphology across a wide range of generations, genders, and races, rather than solely focusing on the elderly population.…”

“…However, in recent years, several studies have reported that vertebral body morphology differs based on race, [32][33][34] gender, [33] and age. [34] Additionally, several studies have shown slight vertebral body crushing even in relatively young people [21,35]; we believe that it is necessary to understand the reference range of vertebral body morphology across a wide range of generations, genders, and races, rather than solely focusing on the elderly population. To apply these research results in clinical practice, rapid QM is necessary, which is enabled by the proposed software.…”

Reference Data for Diagnosis of Spondylolisthesis and Disc Space Narrowing Based on NHANES-II X-rays

“…The spondylolisthesis index in Supplementary File S3 utilizes the endplate width ratio to mitigate endplate width discrepancies. NHANES-II vertebral morphology data can help identify abnormalities in morphology [10]. Considering factors like pelvic incidence may also be important when interpreting SPO [41,52].…”

“…Details of landmark placement can be found in Supplementary File S1, as proper placement is crucial for accurate SPO and disc measurements. Vertebral morphology based on these landmarks has been reported previously [10]. While specific software was used to obtain landmarks, the focus of this study was not on the software itself, as the landmarks are intended to be consistent with those used in prior research studies [11][12][13][14][15].…”

Diagnosis of spine pseudoarthrosis based on the biomechanical properties of bone

“…However, this technology was not the focus of the study, as artificial intelligence and machine learning methods have been developed for use in identifying vertebral landmarks by many researchers. (60)(61)(62)(63)(64) The details of landmark placement for this study have been documented previously (65) and are intended to be comparable to those in prior studies. It is anticipated that the landmark placement used for strain measurements in this study can be replicated by alternative methods, making the data valuable for other investigators utilizing alternative methods for obtaining landmarks.…”