Novel Screening Tool for Stroke Using Artificial Neural Network

Abedi, Vida; Goyal, Nitin; Tsivgoulis, Georgios; Hosseinichimeh, Niyousha; Hontecillas, Raquel; Bassaganya‐Riera, Josep; Elijovich, Lucas; Metter, Jeffrey; Alexandrov, Anne W.; Liebeskind, David S; Alexandrov, Andrei V.; Zand, Ramin

doi:10.1161/strokeaha.117.017033

Cited by 92 publications

(76 citation statements)

References 18 publications

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“…However, traditional methods, such as the Cox proportional hazard model [6,8], are unable to effectively explore the complex non-linear relationships in data. Machine learning methods can learn complex structures by incorporating numerous variables with high dimensional data [9]. Excellent performance of these methods has been validated in health service [10] and health outcomes studies [11].…”

Section: Introductionmentioning

confidence: 99%

Stroke Prediction with Machine Learning Methods among Older Chinese

Fang

2020

IJERPH

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Timely stroke diagnosis and intervention are necessary considering its high prevalence. Previous studies have mainly focused on stroke prediction with balanced data. Thus, this study aimed to develop machine learning models for predicting stroke with imbalanced data in an elderly population in China. Data were obtained from a prospective cohort that included 1131 participants (56 stroke patients and 1075 non-stroke participants) in 2012 and 2014, respectively. Data balancing techniques including random over-sampling (ROS), random under-sampling (RUS), and synthetic minority over-sampling technique (SMOTE) were used to process the imbalanced data in this study. Machine learning methods such as regularized logistic regression (RLR), support vector machine (SVM), and random forest (RF) were used to predict stroke with demographic, lifestyle, and clinical variables. Accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves (AUCs) were used for performance comparison. The top five variables for stroke prediction were selected for each machine learning method based on the SMOTE-balanced data set. The total prevalence of stroke was high in 2014 (4.95%), with men experiencing much higher prevalence than women (6.76% vs. 3.25%). The three machine learning methods performed poorly in the imbalanced data set with extremely low sensitivity (approximately 0.00) and AUC (approximately 0.50). After using data balancing techniques, the sensitivity and AUC considerably improved with moderate accuracy and specificity, and the maximum values for sensitivity and AUC reached 0.78 (95% CI, 0.73–0.83) for RF and 0.72 (95% CI, 0.71–0.73) for RLR. Using AUCs for RLR, SVM, and RF in the imbalanced data set as references, a significant improvement was observed in the AUCs of all three machine learning methods (p < 0.05) in the balanced data sets. Considering RLR in each data set as a reference, only RF in the imbalanced data set and SVM in the ROS-balanced data set were superior to RLR in terms of AUC. Sex, hypertension, and uric acid were common predictors in all three machine learning methods. Blood glucose level was included in both RLR and RF. Drinking, age and high-sensitivity C-reactive protein level, and low-density lipoprotein cholesterol level were also included in RLR, SVM, and RF, respectively. Our study suggests that machine learning methods with data balancing techniques are effective tools for stroke prediction with imbalanced data.

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Section: Introductionmentioning

confidence: 99%

Stroke Prediction with Machine Learning Methods among Older Chinese

Fang

2020

IJERPH

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“…The versatility of AI may mean a future where it is integrated into all aspects of healthcare: AI is already in use for early stroke detection via a device that analyzes for abnormal patient movement patterns,9 for stroke diagnosis from neuroimages, and in the emergency setting to differentiate stroke from stroke mimics based on patient presentation, risk factors, and other clinical aspects. 15 The potential scalability of AI has led Esteva et al-who developed AI software that can classify skin lesions at levels on par with dermatologists-to envision their technology reaching billions of people through the universality of smartphones. 16 limitations As the marriage of medicine and AI becomes more profound, questions pertaining to the shortcomings of the technology will need to be answered or at least appreciated.…”

Section: From Identifying Dog Breeds To Diagnosing Diabetic Retinopathymentioning

confidence: 99%

From Identifying Dog Breeds to Diagnosing Diabetic Retinopathy

Veettil

Nynatten²

2019

UWOMJ

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Experts predict advances in artificial intelligence (AI)—the ability of a machine to mimic human cognition—will spark the fourth industrial revolution, but the dawn of a new age in diagnostic medicine may already be on the horizon. Google and others are leveraging deep learning, a subset of AI that aims to imitate the neuronal processing of the human brain, to screen for diseases—such as diabetic retinopathy, cardiovascular disease, brain tumours, skin cancers, and stroke—at unprecedented levels of sensitivity and specificity. Limitations include an inability to wholly substitute for human empathy and touch, a vulnerability for adversarial training, and concerns about interpretability. If hurdles can be properly dealt with, the coalescence of big data and AI research will change how medicine is provided, practiced, and accessed.

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“…Machine learning is a branch of artificial intelligence. Applications of artificial intelligence to stroke are increasing, and include diagnosis of acute ischemic stroke [36], prediction of stroke [37], predicting outcome after endovascular therapy [38], and pervasive health monitoring using smart monitoring devices [39]. Several studies of machine learning techniques to measure the severity of subclinical white matter changes have recently been reported [40], such as automated white matter segmentation algorithms to measure the severity of white matter hyperintensities in lacunar stroke [41,42], support vector machine techniques to classify the burden of perivascular space in the basal ganglia region [43], and cortical and subcortical volumetric segmentation of diffusion tensor imaging feature vectors [44].…”

Section: Recent Advances: Machine Learning Techniquesmentioning

confidence: 99%

Silent brain infarction: a quiet predictor of future stroke

Bang¹

2018

Precis Future Med

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A silent brain infarct (SBI) is defined as imaging or neuropathological evidence of brain infarction without a history of acute neurological dysfunction attributable to the lesion. The number of patients with SBIs is estimated as several-fold higher than the number with clinical stroke. In addition, SBIs have important clinical implications. The presence of SBIs more than doubles the risk of subsequent stroke and dementia. Although most SBIs are lacunes, for which hypertensive small vessel disease is thought to be the main cause, some of them could be embolic in origin. The pathological mechanisms of SBIs and most effective strategies for prevention of future stroke may differ depending on the cause of the SBI. The literature reviewed and cases presented herein underscore the need for application of appropriate workups and therapeutic strategies in patients with SBIs. In this review, the definition, causes, and clinical impact of SBIs are discussed, together with the questions that remain open and recent advances (e.g., machine learning techniques) in the study of SBIs.

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Novel Screening Tool for Stroke Using Artificial Neural Network

Cited by 92 publications

References 18 publications

Stroke Prediction with Machine Learning Methods among Older Chinese

Stroke Prediction with Machine Learning Methods among Older Chinese

From Identifying Dog Breeds to Diagnosing Diabetic Retinopathy

Silent brain infarction: a quiet predictor of future stroke

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