Advancements in Imaging Technology: Do They (or Will They) Equate to Advancements in Our Knowledge of Recovery in Whiplash?

Elliott, James M.; Dayanidhi, Sudarshan; Hazle, Charles; Hoggarth, Mark A.; McPherson, Jacob G.; Sparks, Cheryl; Weber, Kenneth A.

doi:10.2519/jospt.2016.6735

Cited by 10 publications

(6 citation statements)

References 135 publications

(149 reference statements)

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“…However, emerging evidence suggests that MRI findings of pathology are more common in those with low back pain than those without [27,42,43], and the number of findings are associated with worse outcomes [28]. Whether the same holds true for findings on clinically indicated CT or MRI of the cervical region is unknown, yet this study offers some preliminary evidence that multiple, not isolated, findings of pathology may contribute to, if not enhance, existing prognostic models of recovery, especially in the presence of other known risk factors [9,44].…”

Section: Discussionmentioning

confidence: 71%

Does Overall Cervical Spine Pathology Relate to the Clinical Heterogeneity of Chronic Whiplash?

Elliott

Parrish

Walton

et al. 2020

The American Journal of Emergency Medicine

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Background and purpose-There remains limited evidence for the clinical importance of most imaging findings in whiplash. However, it is possible the type and number of findings on Computed Tomography (CT) may contribute to prognostic recovery models. The purpose is to interpret cervical spine pathologies in the context of known factors influencing recovery.Materials and methods-This is a secondary analysis from a database of 97 acutely injured participants enrolled in a prospective inception cohort study. Thirty-eight participants underwent standard of care cervical spine CT in the emergency medicine department. All 38 participants were assessed at <1-week, 2-weeks, and 3-months post-injury and classified using percentage scores on the Neck Disability Index (recovered/mild (NDI of 0-28%) or moderate/severe (NDI ≥ 30%)). Between-group comparison of categorical variables (gender (male/female), presence of at least one CT finding (yes/no), and presence of ≥3 pathologies on CT (yes/no)) was conducted using 2tailed Fisher's exact test.Results-Participants from both groups demonstrated at least one observable pathology. The group with persistent moderate/severe symptoms presented with significantly more pathology at baseline than those who later reported recovery or milder symptoms at 3-months post injury (p = 0.02).

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Section: Discussionmentioning

confidence: 71%

Does Overall Cervical Spine Pathology Relate to the Clinical Heterogeneity of Chronic Whiplash?

Elliott

Parrish

Walton

et al. 2020

The American Journal of Emergency Medicine

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“…Imaging techniques such as fat and water MRI (detailed below) could help quantify the rapid onset of compositional changes in muscle, which may precede macroscopic muscle changes on standard T1-weighted sequences. A preliminary study [30], case-series [94], and interdisciplinary lines of work [95] suggest this may be the case for a subset of patients with whiplash, meaning these advances in imaging techniques could lead to more timely and effective intervention trials and thus, informed clinical decision making.…”

Section: Muscle Fat Infiltration As a Biological Marker Of Diseasementioning

confidence: 99%

Advancing imaging technologies for patients with spinal pain: with a focus on whiplash injury

et al. 2018

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“…Despite ever-increasing options for ‘treatment’ of these conditions, explanations for their persistence have become urgently needed 19 . MFI may be one neurobiological explanation 11,20,21 . However, manual segmentation methods for MFI do not permit for time-efficient monitoring of these muscles in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury

Weber

Smith

Wasielewski

et al. 2019

Sci Rep

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Muscle fat infiltration (MFI) of the deep cervical spine extensors has been observed in cervical spine conditions using time-consuming and rater-dependent manual techniques. Deep learning convolutional neural network (CNN) models have demonstrated state-of-the-art performance in segmentation tasks. Here, we train and test a CNN for muscle segmentation and automatic MFI calculation using high-resolution fat-water images from 39 participants (26 female, average = 31.7 ± 9.3 years) 3 months post whiplash injury. First, we demonstrate high test reliability and accuracy of the CNN compared to manual segmentation. Then we explore the relationships between CNN muscle volume, CNN MFI, and clinical measures of pain and neck-related disability. Across all participants, we demonstrate that CNN muscle volume was negatively correlated to pain (R = −0.415, p = 0.006) and disability (R = −0.286, p = 0.045), while CNN MFI tended to be positively correlated to disability (R = 0.214, p = 0.105). Additionally, CNN MFI was higher in participants with persisting pain and disability (p = 0.049). Overall, CNN’s may improve the efficiency and objectivity of muscle measures allowing for the quantitative monitoring of muscle properties in disorders of and beyond the cervical spine.

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Advancements in Imaging Technology: Do They (or Will They) Equate to Advancements in Our Knowledge of Recovery in Whiplash?

Cited by 10 publications

References 135 publications

Does Overall Cervical Spine Pathology Relate to the Clinical Heterogeneity of Chronic Whiplash?

Does Overall Cervical Spine Pathology Relate to the Clinical Heterogeneity of Chronic Whiplash?

Advancing imaging technologies for patients with spinal pain: with a focus on whiplash injury

Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury

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