Quantitative Evaluation of Stroke Patients’ Wrist Paralysis by Estimation of Kinematic Coefficients and Machine Learning

Objective and Design: We conducted a multinational observational study on features of consecutive acute ischemic stroke (AIS), intracranial hemorrhage (ICH), and cerebral venous or sinus thrombosis (CVST) among SARS-CoV-2 infected patients. Main Outcome Measures: We investigated the association of demographics, clinical data, geographical regions, and countries’ health expenditure among AIS patients with the risk of large vessel occlusion (LVO), stroke severity as measured by National Institute of Health stroke scale (NIHSS), and stroke subtype as measured by the TOAST criteria. Additionally, we applied unsupervised machine learning algorithms to uncover possible similarities among stroke patients. Results: Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least one eligible stroke patient. Out of 432 patients included, 323(74.8%) had AIS, 91(21.1%) ICH, and 18(4.2%) CVST. Among 23 patients with subarachnoid hemorrhage, 16(69.5%) had no evidence of aneurysm. A total of 183(42.4%) patients were women, 104(24.1%) patients were younger than 55 years, and 105(24.4%) patients had no identifiable vascular risk factors. Among 380 patients who had known interval onset of the SARS-CoV-2 and stroke, 144(37.8%) presented to the hospital with chief complaints of stroke-related symptoms, with asymptomatic or undiagnosed SARS-CoV-2 infection. Among AIS patients 44.5% had LVO; 10% had small artery occlusion according to the TOAST criteria. We observed a lower median NIHSS (8[3-17], versus 11[5-17]; p=0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; p<0.001) in countries with middle to high-health expenditure when compared to countries with lower health expenditure. The unsupervised machine learning identified 4 subgroups, with a relatively large group with no or limited comorbidities. Conclusions and Relevance: We observed a relatively high number of young, and asymptomatic SARS-CoV-2 infections among stroke patients. Traditional vascular risk factors were absent among a relatively large cohort of patients. The stroke severity was lower and rate of mechanical thrombectomy was higher among countries with middle to high-health expenditure.

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Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study (Preprint)

10.2196/preprints.20641 ◽

2020 ◽

Author(s):

Eunjeong Park ◽

Kijeong Lee ◽

Taehwa Han ◽

Hyo Suk Nam

Keyword(s):

Machine Learning ◽

Medical Staff ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Grading System ◽

Operating Characteristics ◽

Stroke Patients ◽

Motor Weakness ◽

Automatic Grading

BACKGROUND Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess the symptoms. In the clinical environment, diagnoses and decisions are based on clinical grading methods, including the National Institutes of Health Stroke Scale (NIHSS) score or the Medical Research Council (MRC) score, which have been used to measure motor weakness. Objective grading in various environments is necessitated for consistent agreement among patients, caregivers, paramedics, and medical staff to facilitate rapid diagnoses and dispatches to appropriate medical centers. OBJECTIVE In this study, we aimed to develop an autonomous grading system for stroke patients. We investigated the feasibility of our new system to assess motor weakness and grade NIHSS and MRC scores of 4 limbs, similar to the clinical examinations performed by medical staff. METHODS We implemented an automatic grading system composed of a measuring unit with wearable sensors and a grading unit with optimized machine learning. Inertial sensors were attached to measure subtle weaknesses caused by paralysis of upper and lower limbs. We collected 60 instances of data with kinematic features of motor disorders from neurological examination and demographic information of stroke patients with NIHSS 0 or 1 and MRC 7, 8, or 9 grades in a stroke unit. Training data with 240 instances were generated using a synthetic minority oversampling technique to complement the imbalanced number of data between classes and low number of training data. We trained 2 representative machine learning algorithms, an ensemble and a support vector machine (SVM), to implement auto-NIHSS and auto-MRC grading. The optimized algorithms performed a 5-fold cross-validation and were searched by Bayes optimization in 30 trials. The trained model was tested with the 60 original hold-out instances for performance evaluation in accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). RESULTS The proposed system can grade NIHSS scores with an accuracy of 83.3% and an AUC of 0.912 using an optimized ensemble algorithm, and it can grade with an accuracy of 80.0% and an AUC of 0.860 using an optimized SVM algorithm. The auto-MRC grading achieved an accuracy of 76.7% and a mean AUC of 0.870 in SVM classification and an accuracy of 78.3% and a mean AUC of 0.877 in ensemble classification. CONCLUSIONS The automatic grading system quantifies proximal weakness in real time and assesses symptoms through automatic grading. The pilot outcomes demonstrated the feasibility of remote monitoring of motor weakness caused by stroke. The system can facilitate consistent grading with instant assessment and expedite dispatches to appropriate hospitals and treatment initiation by sharing auto-MRC and auto-NIHSS scores between prehospital and hospital responses as an objective observation.

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Abstract TP215: Machine Learning Analysis of Gene Expression Data and Predictive Stroke Outcomes in Acute Ischemic Stroke

Stroke ◽

10.1161/str.51.suppl_1.tp215 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Sarah R Martha ◽

Qiang Cheng ◽

Liyu Gong ◽

Lisa Collier ◽

Stephanie Davis ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Mechanical Thrombectomy ◽

Stroke Patients ◽

Stroke Outcomes ◽

Inflammatory Genes ◽

Patient Demographics ◽

Edema Volume

Background and Purpose: The ability to predict ischemic stroke outcomes in the first day of admission could be vital for patient counseling, rehabilitation, and care planning. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) collects blood samples distal and proximal to the intracranial thrombus during mechanical thrombectomy. These samples are a novel resource in evaluating acute gene expression changes at the time of ischemic stroke. The purpose of this study was to identify inflammatory genes and patient demographics that are predictive of stroke outcomes (infarct and/or edema volume) in acute ischemic stroke patients. Methods: The BACTRAC study is a non-probability, convenience sampling of subjects (≥ 18 year olds) treated with mechanical thrombectomy for emergent large vessel occlusion. We evaluated relative concentrations of mRNA for gene expression in 84 inflammatory molecules in static blood distal and proximal to the intracranial thrombus from adults who underwent thrombectomy. We employed a machine learning method, Random Forest, utilizing the first set of enrolled subjects, to predict which inflammatory genes and patient demographics were important features for infarct and edema volumes. Results: We analyzed the first 28 subjects (age = 66 ± 15.48, 11 males) in the BACTRAC registry. Results from machine learning analyses demonstrate that the genes CCR4, IFNA2, IL9, CXCL3, Age, DM, IL7, CCL4, BMI, IL5, CCR3, TNF, and IL27 predict infarct volume. The genes IFNA2, IL5, CCL11, IL17C, CCR4, IL9, IL7, CCR3, IL27, DM, and CSF2 predict edema volume. There is an intersection of genes CCR4, IFNA2, IL9, IL7, IL5, CCR3 to both infarct and edema volumes. Overall, these genes depicts a microenvironment for chemoattraction and proliferation of autoimmune cells, particularly Th2 cells and neutrophils. Conclusions: Machine learning algorithms can be employed to develop predictive biomarker signatures for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.

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Abstract WMP23: Automated ASPECTS Scoring of CT Scans for Acute Ischemic Stroke Patients Using Machine Learning

Stroke ◽

10.1161/str.49.suppl_1.wmp23 ◽

2018 ◽

Vol 49 (Suppl_1) ◽

Author(s):

Hulin Kuang ◽

Ericka Teleg ◽

Mohamed Najm ◽

Alexis T Wilson ◽

Sung I Sohn ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Ct Scans ◽

Stroke Patients

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Machine learning volumetry of ischemic brain lesions on CT after thrombectomy—prospective diagnostic accuracy study in ischemic stroke patients

Neuroradiology ◽

10.1007/s00234-020-02419-7 ◽

2020 ◽

Vol 62 (10) ◽

pp. 1239-1245

Author(s):

Jiri Kral ◽

Martin Cabal ◽

Linda Kasickova ◽

Jaroslav Havelka ◽

Tomas Jonszta ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Diagnostic Accuracy ◽

Brain Lesions ◽

Diagnostic Accuracy Study ◽

Stroke Patients ◽

Ischemic Brain ◽

Accuracy Study

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Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2018.2811471 ◽

2018 ◽

Vol 15 (6) ◽

pp. 1953-1959 ◽

Cited By ~ 13

Author(s):

Miguel Monteiro ◽

Ana Catarina Fonseca ◽

Ana Teresa Freitas ◽

Teresa Pinho e Melo ◽

Alexandre P. Francisco ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Functional Outcome ◽

Stroke Patients

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Predicting response to motor therapy in chronic stroke patients using Machine Learning

10.1101/457416 ◽

2018 ◽

Author(s):

Ceren Tozlu ◽

Dylan Edwards ◽

Aaron Boes ◽

Douglas Labar ◽

K. Zoe Tsagaris ◽

...

Keyword(s):

Machine Learning ◽

Evoked Potential ◽

Motor Evoked Potential ◽

Chronic Stroke ◽

Motor Threshold ◽

Stroke Patients ◽

Post Intervention ◽

Machine Learning Methods ◽

Neurophysiological Data ◽

The Difference

AbstractAccurate predictions of motor improvement resulting from intensive therapy in chronic stroke patients is a difficult task for clinicians, but is key in prescribing appropriate therapeutic strategies. Statistical methods, including machine learning, are a highly promising avenue with which to improve prediction accuracy in clinical practice. The first main objective of this study was to use machine learning methods to predict a chronic stroke individual’s motor function improvement after 6 weeks of intervention using pre-intervention demographic, clinical, neurophysiological and imaging data. The second main objective was to identify which data elements were most important in predicting chronic stroke patients’ impairment after 6 weeks of intervention. Data from one hundred and two patients (Female: 31%, age 61±11 years) who suffered first ischemic stroke 3-12 months prior were included in this study. After enrollment, patients underwent 6 weeks of intensive motor and transcranial magnetic stimulation therapy. Age, gender, handedness, time since stroke, pre-intervention Fugl-Meyer Assessment, stroke lateralization, the difference in motor threshold between the unaffected and affected hemispheres, absence or presence of motor evoked potential in the affected hemisphere and various imaging metrics were used as predictors of post-intervention Fugl-Meyer Assessment. Five machine learning methods, including Elastic-Net, Support Vector Machines, Artificial Neural Networks, Classification and Regression Trees, and Random Forest, were used to predict post-intervention Fugl-Meyer Assessment based on either demographic, clinical and neurophysiological data alone or in combination with the imaging metrics. Cross-validated R-squared and root of mean squared error were used to assess the prediction accuracy and compare the performance of methods. Elastic-Net performed significantly better than the other methods for the model containing pre-intervention Fugl-Meyer Assessment, demographic, clinical and neurophysiological data as predictors of post-intervention Fugl-Meyer Assessment (). Pre-intervention Fugl-Meyer Assessment and difference in motor threshold between affected and unaffected hemispheres were commonly found as the strongest two predictors in the clinical model. The difference in motor threshold had greater importance than the absence or presence of motor evoked potential in the affected hemisphere. The various imaging metrics, including lesion overlap with the spinal cord, largely did not improve the model performance. The approach implemented here may enable clinicians to more accurately predict a chronic stroke patient’s individual response to intervention. The predictive models used in this study could assist clinicians in making treatment decisions and improve the accuracy of prognosis in chronic stroke patients.

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Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

Biomedicines ◽

10.3390/biomedicines9101357 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1357

Author(s):

Anthony Winder ◽

Matthias Wilms ◽

Jens Fiehler ◽

Nils D. Forkert

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Treatment Group ◽

Acute Ischemic Stroke ◽

In Silico ◽

Mechanical Thrombectomy ◽

Intravenous Thrombolysis ◽

Stroke Patients ◽

New Devices

Interventional neuroradiology is characterized by engineering- and experience-driven device development with design improvements every few months. However, clinical validation of these new devices requires lengthy and expensive randomized controlled trials. This contribution proposes a machine learning-based in silico study design to evaluate new devices more quickly with a small sample size. Acute diffusion- and perfusion-weighted MRI, segmented one-week follow-up imaging, and clinical variables were available for 90 acute ischemic stroke patients. Three treatment option-specific random forest models were trained to predict the one-week follow-up lesion segmentation for (1) patients successfully recanalized using intra-arterial mechanical thrombectomy, (2) patients successfully recanalized using intravenous thrombolysis, and (3) non-recanalizing patients as an analogue for conservative treatment for each patient in the sample, independent of the true group membership. A repeated-measures analysis of the three predicted follow-up lesions for each patient revealed significantly larger lesions for the non-recanalizing group compared to the successful intravenous thrombolysis treatment group, which in turn showed significantly larger lesions compared to the successful mechanical thrombectomy treatment group (p < 0.001). A groupwise comparison of the true follow-up lesions for the three treatment options showed the same trend but did not reach statistical significance (p = 0.19). We conclude that the proposed machine learning-based in silico trial design leads to clinically feasible results and can support new efficacy studies by providing additional power and potential early intermediate results.

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