A deep learning model for detection of cervical spinal cord compression in MRI scans

Merali, Zamir; Wang, Justin Z; Badhiwala, Jetan H.; Witiw, Christopher D.; Wilson, Jefferson R.; Fehlings, Michael G.

doi:10.1038/s41598-021-89848-3

Cited by 35 publications

(16 citation statements)

References 46 publications

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“…Traditional machine learning algorithms mainly include two steps: feature extraction and classification. In the part of feature extraction, this study selects five most advanced global algorithms for texture representation, namely local binary patterns (LBPs) [ 2 ], local phase quantization (LPQ) [ 12 ], gray-level co-occurrence matrix (GLCM) [ 3 ], histogram of oriented gradient (HOG), and oriented fast and rotated brief (ORB) [ 4 ]. The feature dimensions of the five feature descriptors are shown in Table 3 .…”

Section: Model Experiments and Results Analysismentioning

confidence: 99%

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Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

Wang

Liu

2022

Journal of Healthcare Engineering

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The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of multimodal analgesia nursing on pain control after spinal fusion and to formulate treatment strategies and patients’ health recovery. However, there are still many problems in this aspect, and there is a lack of relevant research and effective methods to assist clinicians in diagnosis. Therefore, to improve the accuracy of computer-aided diagnosis of intervertebral foramen stenosis and the work efficiency of doctors, a deep learning automatic grading algorithm of intervertebral foramen stenosis image is proposed in this study. The image of intervertebral foramen was extracted from the MRI image of sagittal spine, and the image was preprocessed. 86 patients with spinal fusion treated in our hospital, specifically from May 2018 to May 2020, were randomly divided into the control group (routine analgesic nursing) and the multimodal group (multimodal analgesic nursing), with 43 cases in each group. The pain control effect and satisfaction of the two groups were observed. The results after multimodal analgesia nursing show that the VASs of the multimodal group at different time points were significantly lower than those of the control group P < 0.05 ; the satisfaction score of pain control in the multimodal group was significantly higher than that in the control group P < 0.05 . Multimodal analgesia nursing for patients undergoing spinal fusion can effectively reduce the degree of postoperative pain and improve the effect of pain control and satisfaction with pain control, which is worthy of promotion.

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Section: Model Experiments and Results Analysismentioning

confidence: 99%

“…The application and development of multimodal analgesia nursing provide an effective guarantee for clinical analgesia nursing. In recent years, it has been gradually applied to various surgical analgesia and achieved satisfactory results [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

Wang

Liu

2022

Journal of Healthcare Engineering

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“…Merali et al. (2021) developed a DL model for degenerative cervical spinal cord compression on MRI using 201 patients from a surgical database ( 34 ). Their DL model had an overall AUC of 0.94 with a sensitivity of 0.88 and specificity of 0.89.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI

et al. 2022

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BackgroundMetastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.PurposeTo develop a DL model for automated classification of MESCC on MRI.Materials and MethodsPatients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated.ResultsOverall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard.ConclusionA DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

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“…Subsequently, simple and accurate markers are needed to monitor the myelopathy progression and inform surgical decisions ( 7 ). In the past decade, researchers have relied on conventional cervical spine MRI to monitor and determine spinal cord structural changes in DCM patients, e.g., high signal intensity on T2-weighted MR ( 8 , 9 ). Its utility remains controversial as the information obtained from the spinal cord area is limited, i.e., the small cross-sectional area ( 10 – 12 ).…”

Section: Introductionmentioning

confidence: 99%

Brain Structural and Functional Dissociated Patterns in Degenerative Cervical Myelopathy: A Case-Controlled Retrospective Resting-State fMRI Study

Zhou

Shi

2022

Front. Neurol.

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BackgroundPrevious studies have shown the whole-brain global functional connectivity density (gFCD) and gray matter volume (GMV) alterations in patients with degenerative cervical myelopathy (DCM). However, no study aimed to investigate the associations between the spatial patterns of GMV and gFCD alterations in patients with DCM.MethodsStructural data and resting-state functional MRI data of 35 DCM patients and 35 matched healthy controls were collected to assess their gFCD and GMV and investigate gFCD and GMV alterations in patients with DCM and their spatial pattern associations.ResultsIn our current study, significant gFCD and GMV differences were observed in some regions of the visual system, sensorimotor cortices, and cerebellum between patients with DCM and healthy controls. In our findings, decreased gFCD was found in areas primarily located at the sensorimotor cortices, while increased gFCD was observed primarily within areas located at the visual system and cerebellum. Decreased GMV was seen in the left thalamus, bilateral supplementary motor area (SMA), and left inferior occipital cortices in patients with DCM, while increased GMV was observed in the cerebellum.ConclusionOur findings suggest that structural and functional alterations independently contributed to the neuropathology of DCM. However, longitudinal studies are still needed to further illustrate the associations between structural deficits and functional alterations underlying the onset of brain abnormalities as DCM develops.

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A deep learning model for detection of cervical spinal cord compression in MRI scans

Cited by 35 publications

References 46 publications

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI

Brain Structural and Functional Dissociated Patterns in Degenerative Cervical Myelopathy: A Case-Controlled Retrospective Resting-State fMRI Study

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