Age-related changes in thoracic skeletal geometry of elderly females

Holcombe, Sven A.; Wang, Stewart C.; Grotberg, James B.

doi:10.1080/15389588.2017.1309526

Cited by 18 publications

(9 citation statements)

References 17 publications

(22 reference statements)

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“…By definition, degenerative changes develop over time; increased prevalence of degenerative spine pathology with age is therefore expected and is well documented 7,8,12 . Increasing age has been specifically associated with increased spine curvature [13][14][15] , stenosis [15][16][17] , endplate sclerosis, disc degeneration 7 , osteophyte formation, DISH, AS, and reduced bone mineral density 17,18 .…”

Section: Discussionmentioning

confidence: 99%

Prevalence of spine degeneration diagnosis by type, age, gender, and obesity using Medicare data

Parenteau

Lau

Campbell

et al. 2021

Sci Rep

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Identifying the prevalence of degenerative spinal pathologies and relevant demographic risk factors is important for understanding spine injury risk, prevention, treatment, and outcome, and for distinguishing acute injuries from degenerative pathologies. Prevalence data in the literature are often based on small-scale studies focused on a single type of pathology. This study evaluates the prevalence of diagnosis of selected degenerative spinal pathology diagnoses using Medicare insurance claim data in the context of published smaller-scale studies. In addition, the data are used to evaluate whether the prevalence is affected by age, sex, diagnosed obesity, and the use of medical imaging. The Medicare Claims 5% Limited Data Set was queried to identify diagnoses of degenerative spinal pathologies. Unique patient diagnoses per year were further evaluated as a function of age, gender, and obesity diagnosis. Participants were also stratified by coding for radiological imaging accompanying each diagnosis. The overall prevalence of diagnosed spinal degenerative disease was 27.3% and increased with age. The prevalence of diagnosed disc disease was 2.7 times greater in those with radiology. The results demonstrate that degenerative findings in the spine are common, and, since asymptomatic individuals may not receive a diagnosis of degenerative conditions, this analysis likely underestimates the general prevalence of these conditions.

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Section: Discussionmentioning

confidence: 99%

Prevalence of spine degeneration diagnosis by type, age, gender, and obesity using Medicare data

Parenteau

Lau

Campbell

et al. 2021

Sci Rep

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“…( 34 ) The proportion of the cortical thickness to the total diameters of the clavicles, ribs, and thoracic vertebrae decreased with age. ( 34,42,43 ) Kent and colleagues ( 44 ) described that one of the rib changes that occur with age and account for the decrease in injury tolerance in rib fractures in the elderly population was compositional changes (including cross‐sectional cortex area). Several studies showed rib fractures should be considered to be osteoporotic fractures in older population ( 45,46 ) , although Grad‐CAMs of the trained model with whole‐chest images in substudies highlight the lower thoracic and lumbar areas less because the lower thoracic spine area overlaps the heart shade.…”

Section: Discussionmentioning

confidence: 99%

Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation With a Cohort Dataset

Jang

Kim

Bae

et al. 2020

Journal of Bone and Mineral Research

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Osteoporosis is a common, but silent disease until it is complicated by fractures that are associated with morbidity and mortality. Over the past few years, although deep learning‐based disease diagnosis on chest radiographs has yielded promising results, osteoporosis screening remains unexplored. Paired data with 13,026 chest radiographs and dual‐energy X‐ray absorptiometry (DXA) results from the Health Screening and Promotion Center of Asan Medical Center, between 2012 and 2019, were used as the primary dataset in this study. For the external test, we additionally used the Asan osteoporosis cohort dataset (1089 chest radiographs, 2010 and 2017). Using a well‐performed deep learning model, we trained the OsPor‐screen model with labels defined by DXA based diagnosis of osteoporosis (lumbar spine, femoral neck, or total hip T‐score ≤ −2.5) in a supervised learning manner. The OsPor‐screen model was assessed in the internal and external test sets. We performed substudies for evaluating the effect of various anatomical subregions and image sizes of input images. OsPor‐screen model performances including sensitivity, specificity, and area under the curve (AUC) were measured in the internal and external test sets. In addition, visual explanations of the model to predict each class were expressed in gradient‐weighted class activation maps (Grad‐CAMs). The OsPor‐screen model showed promising performances. Osteoporosis screening with the OsPor‐screen model achieved an AUC of 0.91 (95% confidence interval [CI], 0.90–0.92) and an AUC of 0.88 (95% CI, 0.85–0.90) in the internal and external test set, respectively. Even though the medical relevance of these average Grad‐CAMs is unclear, these results suggest that a deep learning‐based model using chest radiographs could have the potential to be used for opportunistic automated screening of patients with osteoporosis in clinical settings. © 2021 American Society for Bone and Mineral Research (ASBMR).

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“…The population used for training may not capture these distinctions. Additionally, the mean age of the patients in this study was 52.20 ± 9.16, and vertebral anatomy has been established to change with age (Holcombe et al 2017). Therefore, caution must be used when assessing different demographics.…”

Section: Discussionmentioning

confidence: 85%

Statistical shape modelling of the thoracic spine for the development of pedicle screw insertion guides

Wai

Rusli

Ghouse

et al. 2022

Biomech Model Mechanobiol

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Spinal fixation and fusion are surgical procedures undertaken to restore stability in the spine and restrict painful or degenerative motion. Malpositioning of pedicle screws during these procedures can result in major neurological and vascular damage. Patient-specific surgical guides offer clear benefits, reducing malposition rates by up to 25%. However, they suffer from long lead times and the manufacturing process is dependent on third-party specialists. The development of a standard set of surgical guides may eliminate the issues with the manufacturing process. To evaluate the feasibility of this option, a statistical shape model (SSM) was created and used to analyse the morphological variations of the T4–T6 vertebrae in a population of 90 specimens from the Visible Korean Human dataset (50 females and 40 males). The first three principal components, representing 39.7% of the variance within the population, were analysed. The model showed high variability in the transverse process (~ 4 mm) and spinous process (~ 4 mm) and relatively low variation (< 1 mm) in the vertebral lamina. For a Korean population, a standardised set of surgical guides would likely need to align with the lamina where the variance in the population is lower. It is recommended that this standard set of surgical guides should accommodate pedicle screw diameters of 3.5–6 mm and transverse pedicle screw angles of 3.5°–12.4°.

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Age-related changes in thoracic skeletal geometry of elderly females

Cited by 18 publications

References 17 publications

Prevalence of spine degeneration diagnosis by type, age, gender, and obesity using Medicare data

Prevalence of spine degeneration diagnosis by type, age, gender, and obesity using Medicare data

Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation With a Cohort Dataset

Statistical shape modelling of the thoracic spine for the development of pedicle screw insertion guides

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