Artificial Neuroplasticity with Deep Learning Reconstruction Signals to Reconnect Motion Signals for the Spinal Cord

2019 ◽  
pp. 11-20
Author(s):  
Ricardo Jaramillo Díaz ◽  
Laura Veronica Jaramillo Marin ◽  
María Alejandra Barahona García
Keyword(s):  
Spinal Cord ◽  
Deep Learning

scholarly journals Deep Learning Versus Logistic Regression to Predict Opioid Dose Reduction After Spinal Cord Stimulation

Neurosurgery ◽  
2020 ◽  
Vol 67 (Supplement_1) ◽  
Author(s):  
Syed M Adil ◽  
Lefko T Charalambous ◽  
Kelly R Murphy ◽  
Shervin Rahimpour ◽  
Stephen C Harward ◽  
...  
Keyword(s):  
Neural Network ◽  
Spinal Cord ◽  
Deep Learning ◽  
Dose Reduction ◽  
Spinal Cord Stimulation ◽  
Network Architecture ◽  
Characteristic Curve ◽  
Model Performance ◽  
Health Crisis ◽  
Opioid Dose

Abstract INTRODUCTION Opioid misuse persists as a public health crisis affecting approximately one in four Americans.1 Spinal cord stimulation (SCS) is a neuromodulation strategy to treat chronic pain, with one goal being decreased opioid consumption. Accurate prognostication about SCS success is key in optimizing surgical decision making for both physicians and patients. Deep learning, using neural network models such as the multilayer perceptron (MLP), enables accurate prediction of non-linear patterns and has widespread applications in healthcare. METHODS The IBM MarketScan® (IBM) database was queried for all patients ≥ 18 years old undergoing SCS from January 2010 to December 2015. Patients were categorized into opioid dose groups as follows: No Use, ≤ 20 morphine milligram equivalents (MME), 20–50 MME, 50–90 MME, and >90 MME. We defined “opiate weaning” as moving into a lower opioid dose group (or remaining in the No Use group) during the 12 months following permanent SCS implantation. After pre-processing, there were 62 predictors spanning demographics, comorbidities, and pain medication history. We compared an MLP with four hidden layers to the LR model with L1 regularization. Model performance was assessed using area under the receiver operating characteristic curve (AUC) with 5-fold nested cross-validation. RESULTS Ultimately, 6,124 patients were included, of which 77% had used opioids for >90 days within the 1-year pre-SCS and 72% had used >5 types of medications during the 90 days prior to SCS. The mean age was 56 ± 13 years old. Collectively, 2,037 (33%) patients experienced opiate weaning. The AUC was 0.74 for the MLP and 0.73 for the LR model. CONCLUSION To our knowledge, we present the first use of deep learning to predict opioid weaning after SCS. Model performance was slightly better than regularized LR. Future efforts should focus on optimization of neural network architecture and hyperparameters to further improve model performance. Models should also be calibrated and externally validated on an independent dataset. Ultimately, such tools may assist both physicians and patients in predicting opioid dose reduction after SCS.

scholarly journals Automatic Multiclass Intramedullary Spinal Cord Tumor Segmentation on MRI with Deep Learning

2021 ◽  
pp. 102766
Author(s):  
Andreanne Lemay ◽  
Charley Gros ◽  
Zhizheng Zhuo ◽  
Jie Zhang ◽  
Yunyun Duan ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Deep Learning ◽  
Spinal Cord Tumor ◽  
Tumor Segmentation ◽  
Intramedullary Spinal Cord ◽  
Intramedullary Spinal Cord Tumor

scholarly journals Inferring Grasp Intentions from Arm Trajectories via Deep Learning to Enable Functional Movement in Quadriplegia Revised Title Determining Grasp Selection from Arm Trajectories via Deep Learning to Enable Functional Hand Movement in Tetraplegia

2020 ◽  
Author(s):  
Nikunj Bhagat ◽  
Kevin King ◽  
Richard Ramdeo ◽  
Adam Stein ◽  
Chad Bouton
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Deep Learning ◽  
Inertial Sensor ◽  
Hand Movement ◽  
Three Dimensional ◽  
High Accuracy ◽  
Reaching Movements ◽  
Neuromuscular Stimulation ◽  
Cord Injury

Abstract Background: Cervical spinal cord injury severely affects grasping ability of its survivors. Fortunately, many individuals with tetraplegia retain residual arm movements that allow them to reach for objects. We propose a wearable technology that utilizes arm movement trajectory information and deep learning methods to determine grasp selection. Furthermore, we combined this approach with neuromuscular stimulation to determine if self-driven functional hand movement could be enabled in spinal cord injury participants.Methods: Two cervical SCI participants performed arbitrary and natural reaching movements toward target objects in three-dimensional space, which were recorded using an inertial sensor worn on their wrist. Time series classifiers were trained to recognize the trajectories using either a Dynamic Time Warping (DTW) algorithm or a Long Short-Term Memory (LSTM) recurrent neural network. As an initial proof-of-concept, we demonstrate real-time classification of the arbitrary movements using DTW only (due to its implementation simplicity), which when used in combination with a high density neuromuscular stimulation sleeve with textile electrodes, enabled participants to perform functional grasping.Results: Participants were able to consistently perform arbitrary two-dimensional and three-dimensional arm movements which could be classified with high accuracy. Furthermore, it was found that natural reaching trajectories for two different target objects (requiring two different grasp types) were distinct and also discriminable with high accuracy. In offline comparisons, LSTM (mean accuracies 99%) performed significantly better than DTW (mean accuracies 86% and 83%) for both arbitrary and natural reaching movements, respectively. Type I and II errors occurred more frequently for DTW (up to 60% and 15%, respectively), whereas it stayed under 5% for LSTM. Also, DTW achieved online accuracy of 79%. Conclusions: We demonstrate the feasibility of utilizing arm trajectory information to determine grasp selection using a wearable inertial sensor along with DTW and deep learning methods. Importantly, this technology can be successfully used to control neuromuscular stimulation and restore functional independence to individuals living with paralysis.

scholarly journals Determining Grasp Selection from Arm Trajectories via Deep Learning to Enable Functional Hand Movement in Tetraplegia

2020 ◽  
Author(s):  
Nikunj Bhagat ◽  
Kevin King ◽  
Richard Ramdeo ◽  
Adam Stein ◽  
Chad Bouton
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Deep Learning ◽  
Inertial Sensor ◽  
Hand Movement ◽  
Three Dimensional ◽  
High Accuracy ◽  
Reaching Movements ◽  
Neuromuscular Stimulation ◽  
Cord Injury

Abstract Background: Cervical spinal cord injury severely affects grasping ability of its survivors. Fortunately, many individuals with tetraplegia retain residual arm movements that allow them to reach for objects. We propose a wearable technology that utilizes arm movement trajectory information and deep learning methods to determine grasp selection. Furthermore, we combined this approach with neuromuscular stimulation to determine if self-driven functional hand movement could be enabled in spinal cord injury participants.Methods: Two cervical SCI participants performed arbitrary and natural reaching movements toward target objects in three-dimensional space, which were recorded using an inertial sensor worn on their wrist. Time series classifiers were trained to recognize the trajectories using either a Dynamic Time Warping (DTW) algorithm or a Long Short-Term Memory (LSTM) recurrent neural network. As an initial proof-of-concept, we demonstrate real-time classification of the arbitrary movements using DTW only (due to its implementation simplicity), which when used in combination with a high density neuromuscular stimulation sleeve with textile electrodes, enabled participants to perform functional grasping.Results: Participants were able to consistently perform arbitrary two-dimensional and three-dimensional arm movements which could be classified with high accuracy. Furthermore, it was found that natural reaching trajectories for two different target objects (requiring two different grasp types) were distinct and also discriminable with high accuracy. In offline comparisons, LSTM (mean accuracies 99%) performed significantly better than DTW (mean accuracies 86% and 83%) for both arbitrary and natural reaching movements, respectively. Type I and II errors occurred more frequently for DTW (up to 60% and 15%, respectively), whereas it stayed under 5% for LSTM. Also, DTW achieved online accuracy of 79%. Conclusions: We demonstrate the feasibility of utilizing arm trajectory information to determine grasp selection using a wearable inertial sensor along with DTW and deep learning methods. Importantly, this technology can be successfully used to control neuromuscular stimulation and restore functional independence to individuals living with paralysis.Trial registration: NCT, NCT03385005. Registered September 26, 2017, https://clinicaltrials.gov/ct2/show/NCT03385005

Markerless analysis of hindlimb kinematics in spinal cord-injured mice through deep learning

2021 ◽  
Author(s):  
Yuta Sato ◽  
Takahiro Kondo ◽  
Munehisa Shinozaki ◽  
Reo Shibata ◽  
Narihito Nagoshi ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Deep Learning ◽  
Hindlimb Kinematics ◽  
Spinal Cord Injured

scholarly journals What are the gray and white matter volumes of the human spinal cord?

2020 ◽  
Author(s):  
Simon Henmar ◽  
Erik B. Simonsen ◽  
Rune W. Berg
Keyword(s):  
Spinal Cord ◽  
Deep Learning ◽  
White Matter ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Post Mortem ◽  
Cross Sectional ◽  
Human Spinal Cord ◽  
Matter Volume ◽  
Motor Activities

The gray matter of the spinal cord is the seat of somata of various types of neurons devoted to the sensory and motor activities of the limbs and trunk as well as a part of the autonomic nervous system. The volume of the spinal gray matter is an indicator of the local neuronal processing and this can decrease due to atrophy associated with degenerative diseases and injury. Nevertheless, the absolute volume of the human spinal cord has rarely been reported, if ever. Here, we use high–resolution magnetic resonance imaging, with a cross–sectional resolution of 50 × 50μm2 and a voxel size of 0.0005mm3, to estimate the total gray and white matter volume of a post mortem human female spinal cord. Segregation of gray and white matter was accomplished using deep learning image segmentation. Further, we include data from a male spinal cord of a previously published study. The gray and white matter volumes were found to be 2.87 and 11.33 ml, respectively for the female and 3.55 and 19.33 ml, respectively for a male. The gray and white matter profiles along the vertebral axis were found to be strikingly similar and the volumes of the cervical, thoracic and lumbosacral sections were almost equal.NEW AND NOTEWORTHYHere, we combine high field MRI (9.4T) and deep learning for a post-mortem reconstruction of the gray and white matter in human spinal cords. We report a minuscule total gray matter volume of 2.87 ml for a female and 3.55 ml for a male. For comparison, these volumes correspond approximately to the distal digit of the little finger.

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