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Development of a clinical spasticity scale for evaluation of dogs with chronic thoracolumbar spinal cord injury.

ABSTRACT: OBJECTIVE To develop a spasticity scale for dogs with chronic deficits following severe spinal cord injury (SCI) for use in clinical assessment and outcome measurement in clinical trials. ANIMALS 20 chronically paralyzed dogs with a persistent lack of hind limb pain perception caused by an acute SCI at least 3 months previously. PROCEDURES Spasticity was assessed in both hind limbs via tests of muscle tone, clonus, and flexor and extensor spasms adapted from human scales. Measurement of patellar clonus duration and flexor spasm duration and degree was feasible. These components were used to create a canine spasticity scale (CSS; overall score range, 0 to 18). Temporal variation for individual dogs and interrater reliability were evaluated. Gait was quantified with published gait scales, and CSS scores were compared with gait scores and clinical variables. Owners were questioned regarding spasticity observed at home. RESULTS 20 dogs were enrolled: 18 with no apparent hind limb pain perception and 2 with blunted responses; 5 were ambulatory. Testing was well tolerated, and scores were repeatable between raters. Median overall CSS score was 7 (range, 3 to 11), and flexor spasms were the most prominent finding. Overall CSS score was not associated with age, SCI duration, lesion location, or owner-reported spasticity. Overall CSS score and flexor spasm duration were associated with gait scores. CONCLUSIONS AND CLINICAL RELEVANCE The CSS could be used to quantify hind limb spasticity in dogs with chronic thoracolumbar SCI and might be a useful outcome measure. Flexor spasms may represent an integral part of stepping in dogs with severe SCI.

SUBMITTER: Lewis MJ

PROVIDER: S-EPMC7441737 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Development of a clinical spasticity scale for evaluation of dogs with chronic thoracolumbar spinal cord injury.

Lewis Melissa J MJ Olby Natasha J NJ

American journal of veterinary research 20170701 7

OBJECTIVE To develop a spasticity scale for dogs with chronic deficits following severe spinal cord injury (SCI) for use in clinical assessment and outcome measurement in clinical trials. ANIMALS 20 chronically paralyzed dogs with a persistent lack of hind limb pain perception caused by an acute SCI at least 3 months previously. PROCEDURES Spasticity was assessed in both hind limbs via tests of muscle tone, clonus, and flexor and extensor spasms adapted from human scales. Measurement of patellar ...[more]

PMID: 28650240

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Project description:Deep Learning based Convolutional Neural Networks (CNNs) are the state-of-the-art machine learning technique with medical image data. They have the ability to process large amounts of data and learn image features directly from the raw data. Based on their training, these networks are ultimately able to classify unknown data and make predictions. Magnetic resonance imaging (MRI) is the imaging modality of choice for many spinal cord disorders. Proper interpretation requires time and expertise from radiologists, so there is great interest in using artificial intelligence to more quickly interpret and diagnose medical imaging data. In this study, a CNN was trained and tested using thoracolumbar MR images from 500 dogs. T1- and T2-weighted MR images in sagittal and transverse planes were used. The network was trained with unremarkable images as well as with images showing the following spinal cord pathologies: intervertebral disc extrusion (IVDE), intervertebral disc protrusion (IVDP), fibrocartilaginous embolism (FCE)/acute non-compressive nucleus pulposus extrusion (ANNPE), syringomyelia and neoplasia. 2,693 MR images from 375 dogs were used for network training. The network was tested using 7,695 MR images from 125 dogs. The network performed best in detecting IVDPs on sagittal T1-weighted images, with a sensitivity of 100% and specificity of 95.1%. The network also performed very well in detecting IVDEs, especially on sagittal T2-weighted images, with a sensitivity of 90.8% and specificity of 98.98%. The network detected FCEs and ANNPEs with a sensitivity of 62.22% and a specificity of 97.90% on sagittal T2-weighted images and with a sensitivity of 91% and a specificity of 90% on transverse T2-weighted images. In detecting neoplasms and syringomyelia, the CNN did not perform well because of insufficient training data or because the network had problems differentiating different hyperintensities on T2-weighted images and thus made incorrect predictions. This study has shown that it is possible to train a CNN in terms of recognizing and differentiating various spinal cord pathologies on canine MR images. CNNs therefore have great potential to act as a "second eye" for imagers in the future, providing a faster focus on the altered image area and thus increasing workflow in radiology.

Dataset Information

Development of a clinical spasticity scale for evaluation of dogs with chronic thoracolumbar spinal cord injury.

Publications

Development of a clinical spasticity scale for evaluation of dogs with chronic thoracolumbar spinal cord injury.

Similar Datasets

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