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

Elliott, James M.; Hancock, Mark J.; Crawford, Rebecca; Smith, Andrew C.; Walton, David M.

doi:10.1016/j.spinee.2017.06.015

Cited by 9 publications

(10 citation statements)

References 122 publications

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“…As CSM patients likely have muscular neck pain, we examined cervical musculature in CSM patients versus controls to assess muscular changes in the CSM population. Muscle fat infiltration is not a new marker of potential pathology, but surprisingly it's presence on MRI is likely thought of as an emerging marker of injury, and associated physiologic degeneration, of the spinal musculature [29]. MFI is associated with poor outcomes in a variety of spinal pathologies [5][6][7][8][9][10][11], including chronic unilateral low back pain [7,8], disc herniation [9,10], and disc disease [11].…”

Section: Discussionmentioning

confidence: 99%

Fatty infiltration of the cervical multifidus musculature and their clinical correlates in spondylotic myelopathy

Cloney

Smith

Coffey

et al. 2018

Journal of Clinical Neuroscience

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Cervical spondylotic myelopathy (CSM) is among the most common spinal cord disorders of the elderly. Muscle fat infiltration (MFI), a potential pathological sign of muscle adiposity, may contribute to or be associated with pain/disability/impairments in patients with CSM. We examined the relationship between MFI and CSM's clinical manifestations by enrolling nine CSM patients and five aged-matched controls to undergo MRI imaging of the cervical spine with MFI. A blinded investigator calculated MFI for each of the bilateral multifidii muscles from C3 to C7 on the MRI images. Nurick scores, Neck Disability Index, and modified Japanese Orthopedic Association scores were collected for all patients. CSM patients and controls were equivalent with respect to age, height, weight, gender, race, smoking status, and employment status. MFI was higher in patients with CSM than in controls (31.7% v. 24.6%, respectively, p = 0.0178). Higher MFI was associated with increased disability on the Nurick scale (p = 0.0371). MJOA scores correlated linearly with MFI (R = 0.542, p = 0.0453), but not NDI (p = 0.3125). Increased MFI of the multifidus muscles is associated with cervical myelopathy and a clinically significant decline in sensorimotor function as measured by mJOA and Nurick scores. Spinal injury in CSM may lead to secondary muscle loss and muscle fat infiltration.

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Section: Discussionmentioning

confidence: 99%

Fatty infiltration of the cervical multifidus musculature and their clinical correlates in spondylotic myelopathy

Cloney

Smith

Coffey

et al. 2018

Journal of Clinical Neuroscience

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“…However, no consensus exists on which imaging modalities should be used or which patellofemoral joint features are associated with PFP compared to asymptomatic individuals (42). There is, in addition, in the UK a pressure to reduce imaging in clinical practice due to resource constraints, this risks the biological component of the biopsychosocial model being overlooked and potentially stifles research that can improve our understanding of how local joint pathology influences clinical presentation (43). A recent systematic review has demonstrated that a number of MRI features are associated with PFP, i.e., MRI bisect offset and CT congruence angle analysed at 0 knee flexion and 15 knee flexion respectively (42).…”

Section: Pfp Subgroups Derived From Imagingmentioning

confidence: 99%

Anterior knee pain subgroups: the first step towards a personalized treatment

Selfe¹,

Janssen²,

Drew³

et al. 2018

Ann. Joint

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Identification of subgroups within the patellofemoral pain (PFP) population has gained a lot of interest and attention from the research community in recent years due to the recognition of the relatively poor patient outcomes associated with the multimodal approach and following the success of subgrouping approaches used in the management of low back pain. This paper reviews early attempts at PFP subgrouping and introduces readers to some of the modern methodological approaches employed to derive subgroups. Summaries of the results of two research projects illustrating the use of these more robust methods to derive subgroups in the PFP population are provided. In conclusion, it appears there are probably 3 or 4 discrete subgroups within the PFP population that may require a more personalised approach to treatment. However, to date no definitive randomized controlled trials (RCTs) have been conducted to evaluate the potential benefits of targeted interventions for PFP subgroups in terms of improved patient outcomes so this warrants further research.

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“…While plausible to hypothesize that larger amounts of paraspinal MFI negatively impact function, recent studies do not provide confirmation 9,10 . This could be due to the varied methods used to measure MFI and function 11 , as others have shown MFI to be associated with lower physical function 12–14 , and that better surgical outcomes are achieved in those with larger muscle size and better quality 15,16 . Preliminary evidence suggests MFI may be reversible and associated with a concomitant improvement in chronic whiplash-related disability 17 .…”

Section: Introductionmentioning

confidence: 99%

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

Abstract: Advancing imaging technologies may help to explain the seemingly disconnected spectrum of biopsychosocial signs and symptoms of traumatic neck pain.

Cited by 9 publications

References 122 publications

Fatty infiltration of the cervical multifidus musculature and their clinical correlates in spondylotic myelopathy

Fatty infiltration of the cervical multifidus musculature and their clinical correlates in spondylotic myelopathy

Anterior knee pain subgroups: the first step towards a personalized treatment

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

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