Prediction of intracerebral hemorrhage following thrombolytic therapy for acute ischemic stroke using multiple artificial neural networks

Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis

Background: Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accurate prediction to optimize patient care. Methods: A comprehensive search was conducted across PubMed, Web of Science, Scopus, Embase, and Google Scholar, covering studies from inception until July 10, 2024. Studies were included if they used machine learning (ML) or deep learning algorithms to predict HT in AIS patients treated with thrombolysis. Exclusion criteria included studies involving endovascular treatments and those not evaluating model effectiveness. Data extraction and quality assessment were performed following PRISMA guidelines and using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST) tools. Results: Out of 1943 identified records, 12 studies were included in the final analysis, encompassing 18 007 AIS patients who received thrombolytic therapy. The ML models demonstrated high predictive performance, with pooled area under the curve (AUC) values ranging from 0.79 to 0.95. Specifically, XGBoost models achieved AUCs of up to 0.953 and Artificial Neural Network (ANN) models reached up to 0.942. Sensitivity and specificity varied significantly, with the highest sensitivity at 0.90 and specificity at 0.99. Significant predictors of HT included age, glucose levels, NIH Stroke Scale (NIHSS) score, systolic and diastolic blood pressure, and radiomic features. Despite these promising results, methodological disparities and limited external validation highlighted the need for standardized reporting and further rigorous testing. Conclusion: ML techniques, especially XGBoost and ANN, show great promise in predicting HT following thrombolysis in AIS patients, enhancing risk stratification and clinical decision-making. Future research should focus on prospective study designs, standardized reporting, and integrating ML assessments into clinical workflows to improve AIS management and patient outcomes.

Proposed Protocols for Artificial Intelligence Imaging Database in Acute Stroke Imaging

PURPOSE

To propose standardized and feasible imaging protocols for constructing artificial intelligence (AI) database in acute stroke by assessing the current practice at tertiary hospitals in South Korea and reviewing evolving AI models.

MATERIALS AND METHODS

A nationwide survey on acute stroke imaging protocols was conducted using an electronic questionnaire sent to 43 registered tertiary hospitals between April and May 2021. Imaging protocols for endovascular thrombectomy (EVT) in the early and late time windows and during follow-up were assessed. Clinical applications of AI techniques in stroke imaging and required sequences for developing AI models were reviewed. Standardized and feasible imaging protocols for data curation in acute stroke were proposed.

RESULTS

There was considerable heterogeneity in the imaging protocols for EVT candidates in the early and late time windows and posterior circulation stroke. Computed tomography (CT)-based protocols were adopted by 70% (30/43), and acquisition of noncontrast CT, CT angiography and CT perfusion in a single session was most commonly performed (47%, 14/30) with the preference of multiphase (70%, 21/30) over single phase CT angiography. More hospitals performed magnetic resonance imaging (MRI)-based protocols or additional MRI sequences in a late time window and posterior circulation stroke. Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) were most commonly performed MRI sequences with considerable variation in performing other MRI sequences. AI models for diagnostic purposes required noncontrast CT, CT angiography and DWI while FLAIR, dynamic susceptibility contrast perfusion, and T1-weighted imaging (T1WI) were additionally required for prognostic AI models.

CONCLUSION

Given considerable heterogeneity in acute stroke imaging protocols at tertiary hospitals in South Korea, standardized and feasible imaging protocols are required for constructing AI database in acute stroke. The essential sequences may be noncontrast CT, DWI, CT/MR angiography and CT/MR perfusion while FLAIR and T1WI may be additionally required.

Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review

BACKGROUND

Established randomized trial-based parameters for acute ischemic stroke group patients into generic treatment groups, leading to attempts using various artificial intelligence (AI) methods to directly correlate patient characteristics to outcomes and thereby provide decision support to stroke clinicians. We review AI-based clinical decision support systems in the development stage, specifically regarding methodological robustness and constraints for clinical implementation.

METHODS

Our systematic review included full-text English language publications proposing a clinical decision support system using AI techniques for direct decision support in acute ischemic stroke cases in adult patients. We (1) describe data and outcomes used in those systems, (2) estimate the systems' benefits compared with traditional stroke diagnosis and treatment, and (3) reported concordance with reporting standards for AI in healthcare.

RESULTS

One hundred twenty-one studies met our inclusion criteria. Sixty-five were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous.

CONCLUSIONS

Our results suggest significant validity threats, dissonance in reporting practices, and challenges to clinical translation. We outline practical recommendations for the successful implementation of AI research in acute ischemic stroke treatment and diagnosis.

A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

Background

Thrombolysis is the first-line treatment for patients with acute ischemic stroke. Previous studies leveraged machine learning to assist neurologists in selecting patients who could benefit the most from thrombolysis. However, when designing the algorithm, most of the previous algorithms traded interpretability for predictive power, making the algorithms hard to be trusted by neurologists and be used in real clinical practice.

Methods

Our proposed algorithm is an advanced version of classical k-nearest neighbors classification algorithm (KNN). We achieved high interpretability by changing the isotropy in feature space of classical KNN. We leveraged a cohort of 189 patients to prove that our algorithm maintains the interpretability of previous models while in the meantime improving the predictive power when compared with the existing algorithms. The predictive powers of models were assessed by area under the receiver operating characteristic curve (AUC).

Results

In terms of interpretability, only onset time, diabetes, and baseline National Institutes of Health Stroke Scale (NIHSS) were statistically significant and their contributions to the final prediction were forced to be proportional to their feature importance values by the rescaling formula we defined. In terms of predictive power, our advanced KNN (AUC 0.88) outperformed the classical KNN (AUC 0.75, p = 0.0192 ).

Conclusions

Our preliminary results show that the advanced KNN achieved high AUC and identified consistent significant clinical features as previous clinical trials/observational studies did. This model shows the potential to assist in thrombolysis patient selection for improving the successful rate of thrombolysis.

The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements

In the treatment of ischemic stroke, timely and efficient recanalization of occluded brain arteries can successfully salvage the ischemic brain. Thrombolysis is the first-line treatment for ischemic stroke. Machine learning models have the potential to select patients who could benefit the most from thrombolysis. In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and feasibility needs to be achieved by elaborate computer science algorithms to increase the interpretability of flexible algorithms and shorten the processing time of the pipeline interpreting medical images.

Machine learning prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis: a cross-cultural validation in Caucasian and Han Chinese cohort

Background

Previous studies found that Asians seemed to have higher risk of HT after thrombolysis than Caucasians due to its race differences in genetic polymorphism. Whether the model developed by Caucasians could predict risk of symptomatic intracerebral hemorrhage (sICH) in Asians was unknown.

Objectives

To develop a machine learning-based model for predicting sICH after stroke thrombolysis in Caucasians and externally validate it in an independent Han Chinese cohort.

Design

The derivation Caucasian sample included 1738 ischemic stroke (IS) patients from the Virtual International Stroke Trials Archive (VISTA) data set, and the external validation Han Chinese cohort included 296 IS patients who were treated with intravenous thrombolysis.

Methods

Twenty-eight variables were collected across both samples. According to their properties, we classified them into six distinct clusters (ie, demographic variables, medical history, previous medication, baseline blood biomarkers, neuroimaging markers on initial CT scan and clinical characteristics). A support vector machine (SVM) model, which consisted of data processing, model training, testing and a 10-fold cross-validation, was developed to predict the risk of sICH after stroke thrombolysis. The receiving operating characteristic (ROC) was used to assess the prediction performance of the SVM model. A domain contribution analysis was then performed to test which cluster had the highest influence on the performance of the model.

Results

In total, 85 (4.9%) patients developed sICH in the Caucasians, and 29 (9.8%) patients developed sICH in the Han Chinese cohort. Eight features including age, NIHSS score, SBP (systolic blood pressure), DBP (diastolic blood pressure), ALP (alkaline phosphatase), ALT (alanine transaminase), glucose, and creatine level were included in the final model, all of which were from demographic, clinical characteristics, and blood biomarkers clusters, respectively. The SVM model showed a good predictive performance in both Caucasians (AUC = 0.87) and Han Chinese patients (AUC = 0.74). Domain contribution analysis showed that inclusion/exclusion of clinical characteristic cluster (NIHSS score, SBP, and DBP), had the highest influence on the performance of predicting sICH in both Caucasian and Han Chinese cohorts.

Conclusion

The established SVM model is feasible for predicting the risk of sICH after thrombolysis quickly and efficiently in both Caucasian and Han Chinese cohort.

Neuroimaging Prediction of Hemorrhagic Transformation for Acute Ischemic Stroke

BACKGROUND

Hemorrhagic transformation (HT) is a common complication of acute ischemic stroke, often resulting from reperfusion therapy. Early prediction of HT can enable stroke neurologists to undertake measures to avoid clinical deterioration and make optimal treatment strategies. Moreover, the trend of extending the time window for reperfusion therapy (both for intravenous thrombolysis and endovascular treatment) further requires more precise detection of HT tendency.

SUMMARY

In this review, we summarized and discussed the neuroimaging markers of HT prediction of acute ischemic stroke patients, mainly focusing on neuroimaging markers of ischemic degree and neuroimaging markers of blood-brain barrier permeability. This review is aimed to provide a concise introduction of HT prediction and to elicit possibilities of future research combining advanced technology to improve the accessibility and accuracy of HT prediction under emergent clinical settings. Key Messages: Substantial studies have utilized neuroimaging, blood biomarkers, and clinical variables to predict HT occurrence. Although huge progress has been made, more individualized and precise HT prediction using simple and robust imaging predictors combining stroke onset time should be the future goal of development.

Personalized risk prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis using a machine-learning model

Background:

Personalized prediction of the risk of symptomatic intracerebral hemorrhage (sICH) after stroke thrombolysis is clinically useful. Machine-learning-based modeling may provide the personalized prediction of the risk of sICH after stroke thrombolysis.

Methods:

We identified 2578 thrombolysis-treated ischemic stroke patients between January 2013 and December 2016 from a multicenter database, where 70% were used to train models and the remaining 30% were used as the nominal test sets. Another 136 consecutive tissue plasminogen-activated-treated patients between January 2017 and December 2017 from our institute were enrolled as the independent test sets for clinical usability evaluation. Five machine-learning models were developed to predict the risk of sICH after stroke thrombolysis, and the receiving operating characteristic (ROC) was used to compare the prediction performance.

Results:

In total, 2237 cases were included in our study, of which 102 had sICH transformation (4.56%). Finally, the three-layer neuro network was selected with the best performance on nominal test sets (AUC = 0.82). The probability of the model score was further categorized into three risk ranks (18.97%, 5.63%, and 0.81%) according to the risk distribution. Implementing our system in clinical practice was associated with reduced computed tomography (CT)-to-treatment time (CTT; 41 min versus 52 min, p < 0.001). All sICH patients were correctly predicted to be within the high-sICH risk rank.

Conclusions:

The machine-learning-based modeling is feasible for providing personalized risk prediction of sICH after stroke thrombolysis, and is able to reduce the CTT. More data are needed to further optimize the model and improve the accuracy of prediction.

The combination of three molecular markers can be a valuable predictive tool for the prognosis of hepatocellular carcinoma patients

Based on molecular profiling, several prognostic markers for HCC are also used in clinic, but only a few genes have been identified as useful. We collected 72 post-operative liver cancer tissue samples. Genes expression were tested by RT-PCR. Multilayer perceptron and discriminant analysis were built, and their ability to predict the prognosis of HCC patients were tested. Receiver operating characteristic (ROC) analysis was performed and multivariate analysis with Cox’s Proportional Hazard Model was used for confirming the markers’predictive efficiency for HCC patients’survival. A simple risk scoring system devised for further predicting the prognosis of liver tumor patients. Multilayer perceptron and discriminant analysis showed a very strong predictive value in evaluating liver cancer patients’prognosis. Cox multivariate regression analysis demonstrated that DUOX1, GLS2, FBP1 and age were independent risk factors for the prognosis of HCC patients after surgery. Finally, the risk scoring system revealed that patients whose total score >1 and >3 are more likely to relapse and die than patients whose total score ≤1 and ≤3. The three genes model proposed proved to be highly predictive of the HCC patients’ prognosis. Implementation of risk scoring system in clinical practice can help in evaluating survival of HCC patients after operation.

Meteorological Factors Related to Emergency Admission of Elderly Stroke Patients in Shanghai: Analysis with a Multilayer Perceptron Neural Network

Background

The aim of this study was to predict the emergency admission of elderly stroke patients in Shanghai by using a multilayer perceptron (MLP) neural network.

Material/Methods

Patients (>60 years) with first-ever stroke registered in the Emergency Center of Neurology Department, Shanghai Tenth People’s Hospital, from January 2012 to June 2014 were enrolled into the present study. Daily climate records were obtained from the National Meteorological Office. MLP was used to model the daily emergency admission into the neurology department with meteorological factors such as wind level, weather type, daily maximum temperature, lowest temperature, average temperature, and absolute temperature difference. The relationships of meteorological factors with the emergency admission due to stroke were analyzed in an MLP model.

Results

In 886 days, 2180 first-onset elderly stroke patients were enrolled, and the average number of stroke patients was 2.46 per day. MLP was used to establish a model for the prediction of dates with low stroke admission (≤4) and those with high stroke admission (≥5). For the days with low stroke admission, the absolute temperature difference accounted for 40.7% of admissions, while for the days with high stroke admission, the weather types accounted for 73.3%.

Conclusions

Outdoor temperature and related meteorological parameters are associated with stroke attack. The absolute temperature difference and the weather types have adverse effects on stroke. Further study is needed to determine if other meteorological factors such as pollutants also play important roles in stroke attack.

Prediction of stroke thrombolysis outcome using CT brain machine learning

A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis — a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH). Certain imaging features based upon early computerized tomography (CT), in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH). The sample was split into training (n = 106) and test sets (n = 10), repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM), along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort). Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC), of the SVM (0.744) compared favourably with that of prognostic scores (original and adapted versions: 0.626–0.720; p < 0.01). The SVM also identified 9 out of 16 SICHs, as opposed to 1–5 using prognostic scores, assuming a 10% SICH frequency (p < 0.001). In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods.

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Machine learning of acute stroke CTs may predict thrombolysis-associated haemorrhage.

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CT machine learning also circumvents high variability of radiologist interpretations.

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Favourable performance of CT machine learning reported here warrants further research.

A Support Vector Machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins

Background

Members of the phylum Proteobacteria are most prominent among bacteria causing plant diseases that result in a diminution of the quantity and quality of food produced by agriculture. To ameliorate these losses, there is a need to identify infections in early stages. Recent developments in next generation nucleic acid sequencing and mass spectrometry open the door to screening plants by the sequences of their macromolecules. Such an approach requires the ability to recognize the organismal origin of unknown DNA or peptide fragments. There are many ways to approach this problem but none have emerged as the best protocol. Here we attempt a systematic way to determine organismal origins of peptides by using a machine learning algorithm. The algorithm that we implement is a Support Vector Machine (SVM).

Result

The amino acid compositions of proteobacterial proteins were found to be different from those of plant proteins. We developed an SVM model based on amino acid and dipeptide compositions to distinguish between a proteobacterial protein and a plant protein. The amino acid composition (AAC) based SVM model had an accuracy of 92.44% with 0.85 Matthews correlation coefficient (MCC) while the dipeptide composition (DC) based SVM model had a maximum accuracy of 94.67% and 0.89 MCC. We also developed SVM models based on a hybrid approach (AAC and DC), which gave a maximum accuracy 94.86% and a 0.90 MCC. The models were tested on unseen or untrained datasets to assess their validity.

Conclusion

The results indicate that the SVM based on the AAC and DC hybrid approach can be used to distinguish proteobacterial from plant protein sequences.