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.

BackgroundA robust definition of normal is required to confidently identify vertebral abnormalities such as fractures. Between 1976 and 1980, the 2nd National Health and Nutrition Examination Survey (NHANES-II) was conducted. Justified by the prevalence of neck and back pain, approximately 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. This resource can be used for establishing normative reference data that can subsequently be used to diagnose abnormal vertebral morphology.Purpose1) Develop normative reference data for vertebral morphology using the lateral spine radiographs from NHANES-II. 2) Document sources of variability.Subject SampleNationwide probability sample to document health status of the United States.MethodsThe coordinates of the four vertebral body corners were obtained using previously validated, automated technology consisting of a proprietary pipeline of neural networks and coded logic. These landmarks were used to calculate six vertebral body morphology metrics: 1) anterior/posterior vertebral body height ratio (VBHR); 2) superior/inferior endplate width ratio (EPWR); 3) forward/backward diagonal ratio (FBDR); 4) height/width ratio (HWR); 5: angle between endplates (EPA); 6) Angle between posterior wall and superior endplate (PSA). Descriptive statistics were generated and used to identify and trim outliers from the data and obtain a gaussian distribution for each metric. 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 BMI was also assessed.ResultsComputer generated lumbar landmarks were obtained for 42,980 vertebrae from lumbar radiographs and 54,093 vertebrae from cervical radiographs for subjects 25 to 74 years old. After removing outliers, means and standard deviations for the remaining 35,275 lumbar and 44,938 cervical vertebrae changed only slightly, suggesting that normal morphology and intervertebral alignment is dominant in the data. There was low variation in vertebral morphology after accounting for vertebra (L1, L2, etc.), and the R2 was high for analyses of variance. The EPWR, FBDR and PSA generally had the lowest coefficients of variation. 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 R2 very little (e.g. for the lumbar data, VBHR R2 went from 0.804 to 0.795 and FBDR R2 went from 0.9005 to 0.9000). Reference data were generated that can be used to produce standardized metrics in units of standard deviation from average. This allows for easy identification of abnormalities resulting from vertebral fractures, atypical vertebral body morphologies, and other congenital or degenerative conditions. Standardized metrics also remove the effect of vertebra thereby enabling data for all vertebrae to be pooled in research studies.ConclusionsThe NHANES-II collection of spine radiographs and associated data may prove to be a valuable resource that can facilitate standardized spine metrics useful for objectively identifying abnormalities. The data may be particularly valuable for identification of vertebral fractures, although X-rays taken early in life would be needed in some cases to differentiate between normal anatomic variants, fractures, and vertebral shape remodeling.

We found preferentially larger motion at the superior bearing of the CHARITÉ discs implanted in human cadaveric lumbar spines and in patients, regardless of the implanted level.

Introduction: Knee joint space narrowing (JSN) is a primary outcome measure in the progression of knee osteoarthritis and its treatment. JSN is most frequently determined from radiographs relying on the radiographic shadow of the anterior or posterior margins of the tibial plateau. Several studies address the confounding issues related to use of the anterior and posterior margins of the tibial plateau. An alternative strategy is to identify the mid-coronal plane of the tibial plateau and use that as a reference for measuring JSN. Methods: Radiographs with precisely defined changes in joint space width (JSW) were created from CT imaging of cadaver knees. The mid-coronal plane of the knee was used to measure JSW and calculate JSN. Radiographs and data from the Osteoarthritis Initiative study were used to assess reproducibility and compare mid-coronal plane measurements of JSN to previously reported methods. Results:The average absolute error in the measured versus known medial JSN was below 0.2 mm. The reproducibility was similar to previously published methods. There was a strong correlation between the mid-coronal plane measurements of JSN and JSN calculated using previously reported methods. There were several discrepancies between the two methods, suggesting that JSN for individual cases may depend on the method used to measure JSN. Discussion: This study describes an alternative to using the margins of the tibial plateau when calculating JSN. JSN measurements based on the mid-coronal plane of the knee, where cartilage changes are more likely to occur, may have advantages.

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