Letters

Melloh, Markus; Lord, Sarah J.; Staub, Lukas; Merk, H.; Barz, Thomas

doi:10.1097/brs.0b013e31828fca2c

Cited by 3 publications

(1 citation statement)

References 1 publication

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“…The authors of the original SedSign study have recently commented that the utility of the Sign may lie in providing additional clinically significant information for patients who meet current diagnostic criteria for LSS to define subgroups that may respond to different treatment protocols, including surgery. 15 It may be that the SedSign is related to treatment outcome for individuals with LSS, yet more research is warranted to determine whether or not this is the case.…”

Section: Discussionmentioning

confidence: 99%

Nerve Root Sedimentation Sign for the Diagnosis of Lumbar Spinal Stenosis

Tomkins-Lane¹,

Quint²,

Gabriel³

et al. 2013

Spine

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Study Design Retrospective review of MRI images. Objective Examine the diagnostic accuracy, discriminative ability, and reliability of the Sedimentation Sign in a sample of patients with clinically diagnosed lumbar spinal stenosis (LSS), low back pain (LBP), vascular claudication, and in asymptomatic controls. Summary of Background Data The Nerve Root Sedimentation Sign (SedSign) was recently described as a new diagnostic test for LSS; however the degree to which this Sign is sensitive and specific in diagnosis of LSS is unknown. Methods All LSS images were from subjects who had clinically diagnosed LSS confirmed on imaging by a spine specialist. The other images were from people with LBP but no LSS, people with severe vascular claudication, and asymptomatic participants. Three blinded raters independently assessed the images.. A positive Sign was defined as the absence of nerve root sedimentation at the level above or below the level of maximum stenosis. Results Images from 148 subjects were reviewed (67 LSS, 31 LBP, 4 vascular, and 46 asymptomatic). Intra-rater reliability for the Sign ranged from kappa = 0.87 to 0.97, and inter-rater reliability from 0.62 to 0.69. Sensitivity ranged from 42–66%, and specificity from 49–78%. Sensitivity improved to a range of 60–96% when only images with a smallest cross sectional area of the dural sac <80mm2 were included. The Sign was able to differentiate (p=0.004) between LSS and asymptomatic controls, but not between LSS and LBP, or between LSS and vascular claudication. Conclusion The SedSign was shown to have high intra-rater reliability and acceptable inter-rater reliability. The Sign appears most sensitive in defining severe LSS cases, yet may not aid in the differential diagnosis of LSS from LBP or vascular claudication, or add any specific diagnostic information beyond the traditional history, physical examination and imaging studies that are standard in LSS diagnosis.

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Section: Discussionmentioning

confidence: 99%

Nerve Root Sedimentation Sign for the Diagnosis of Lumbar Spinal Stenosis

Tomkins-Lane¹,

Quint²,

Gabriel³

et al. 2013

Spine

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Re: Fazal A, Yoo A, Bendo JA. Does the presence of the nerve root sedimentation sign on MRI correlate with the operative level in patients undergoing posterior lumbar decompression for lumbar stenosis? Spine J 2013;13:837–42

Melloh¹,

Tomkins-Lane²,

Quint

et al. 2014

The Spine Journal

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Development of a Deep-Learning Model for Diagnosing Lumbar Spinal Stenosis Based on CT Images

Li,

Weng,

et al. 2023

Spine

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Study Design. Retrospective study. Objectives. This study aimed to develop an initial deep learning model based on CT scans for diagnosing lumbar spinal stenosis. Summary of Background Data. MRI is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional coverage. Using deep learning models to improve the accuracy of CT diagnosis can effectively reduce missed diagnoses and misdiagnoses in clinical practice. Methods. Axial lumbar spine CT scans obtained between March 2022 and September 2023 were included. The dataset was divided into a training set (62.3%), a validation set (22.9%), and a control set (14.8%). All data were labeled by two spine surgeons using the widely accepted grading system for lumbar spinal stenosis. The training and validation sets were used to annotate the ROIs by the two spine surgeons. First, an ROI detection model and a CNN classifier were trained using the training set. After training, the model was preliminarily evaluated using a validation set. Finally, the performance of the deep learning model was evaluated on the control set, and a comparison was made between the model and classification performance of specialists with varying levels of experience. Results. The central stenosis grading accuracies of DL Model Version 1 and DL Model Version 2 were 88% and 83%, respectively. The lateral recess grading accuracies of DL Model Version 1 and DL Model Version 2 were 75% and 71%, respectively. Conclusions. Our preliminarily developed deep learning system for assessing the degree of lumbar spinal stenosis in CT, including the central canal and lateral recess, has shown similar accuracy to experienced specialist physicians. This holds great value for further development and clinical application.

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Letters

Cited by 3 publications

References 1 publication

Nerve Root Sedimentation Sign for the Diagnosis of Lumbar Spinal Stenosis

Nerve Root Sedimentation Sign for the Diagnosis of Lumbar Spinal Stenosis

Re: Fazal A, Yoo A, Bendo JA. Does the presence of the nerve root sedimentation sign on MRI correlate with the operative level in patients undergoing posterior lumbar decompression for lumbar stenosis? Spine J 2013;13:837–42

Development of a Deep-Learning Model for Diagnosing Lumbar Spinal Stenosis Based on CT Images

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