Uncertainty-aware deep learning for trustworthy prediction of long-term outcome after endovascular thrombectomy … – Nature.com
Global Burden of Disease Stroke Expert Group and others. Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016. N. Engl. J. Med. 379, 24292437 (2018).
Article Google Scholar
Goyal, M. et al. Endovascular thrombectomy after large-vessel Ischaemic stroke: A meta-analysis of individual patient data from five randomised trials. Lancet 387, 17231731 (2016).
Article PubMed Google Scholar
Albers, G. W. et al. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N. Engl. J. Med. 378, 708718 (2018).
Article PubMed PubMed Central Google Scholar
Nogueira, R. G. et al. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N. Engl. J. Med. 378, 1121 (2018).
Article PubMed Google Scholar
Quinn, T., Dawson, J., Walters, M. & Lees, K. Functional outcome measures in contemporary stroke trials. Int. J. Stroke 4, 200205 (2009).
Article CAS PubMed Google Scholar
Johnston, K. C., Wagner, D. P., Haley, E. C. Jr. & Connors, A. F. Jr. Combined clinical and imaging information as an early stroke outcome measure. Stroke 33, 466472 (2002).
Article PubMed PubMed Central Google Scholar
Asadi, H., Dowling, R., Yan, B. & Mitchell, P. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS ONE 9, e88225 (2014).
Article ADS PubMed PubMed Central Google Scholar
Monteiro, M. et al. Using machine learning to improve the prediction of functional outcome in ischemic stroke patients. IEEE/ACM Trans. Comput. Biol. Bioinf. 15, 19531959 (2018).
Article Google Scholar
Heo, J. et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke 50, 12631265 (2019).
Article PubMed Google Scholar
Bacchi, S. et al. Deep learning in the prediction of Ischaemic stroke thrombolysis functional outcomes: A pilot study. Acad. Radiol. 27, e19e23 (2020).
Article PubMed Google Scholar
Alaka, S. A. et al. Functional outcome prediction in ischemic stroke: A comparison of machine learning algorithms and regression models. Front. Neurol. 11, 889 (2020).
Article PubMed PubMed Central Google Scholar
Begoli, E., Bhattacharya, T. & Kusnezov, D. The need for uncertainty quantification in machine-assisted medical decision making. Nat. Mach. Intell. 1, 2023 (2019).
Article Google Scholar
Kim, D.-Y. et al. Deep learning-based personalised outcome prediction after acute ischaemic stroke. J. Neurol. Neurosurg. Psychiatry 94, 369378 (2023).
Article PubMed Google Scholar
Vora, N. A. et al. A 5-item scale to predict stroke outcome after cortical middle cerebral artery territory infarction: Validation from results of the diffusion and perfusion imaging evaluation for understanding stroke evolution (defuse) study. Stroke 42, 645649 (2011).
Article PubMed Google Scholar
Panni, P. et al. Acute stroke with large ischemic core treated by thrombectomy: Predictors of good outcome and mortality. Stroke 50, 11641171 (2019).
Article PubMed Google Scholar
Van Os, H. J. et al. Predicting outcome of endovascular treatment for acute ischemic stroke: Potential value of machine learning algorithms. Front. Neurol. 9, 784 (2018).
Article PubMed PubMed Central Google Scholar
Xie, Y. et al. Use of gradient boosting machine learning to predict patient outcome in acute ischemic stroke on the basis of imaging, demographic, and clinical information. Am. J. Roentgenol. 212, 4451 (2019).
Article Google Scholar
Thakkar, H. K., Liao, W.-W., Wu, C.-Y., Hsieh, Y.-W. & Lee, T.-H. Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches. J. Neuroeng. Rehabil. 17, 110 (2020).
Article Google Scholar
Shao, H. et al. A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study. Digit. Health 9, 20552076221149530 (2023).
PubMed PubMed Central Google Scholar
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A. & Vandergheynst, P. Geometric deep learning: Going beyond Euclidean data. IEEE Signal Process. Mag. 34, 1842 (2017).
Article ADS Google Scholar
Parisot, S. et al. Spectral graph convolutions for population-based disease prediction. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Parisot, S. et al.) 177185 (Springer, 2017).
Google Scholar
Kazi, A. et al. Inceptiongcn: Receptive field aware graph convolutional network for disease prediction. In International Conference on Information Processing in Medical Imaging (eds Kazi, A. et al.) 7385 (Springer, 2019).
Chapter Google Scholar
Ravindra, N., Sehanobish, A., Pappalardo, J.L., Hafler, D.A. & van Dijk, D. Disease state prediction from single-cell data using graph attention networks. In: Proc. of the ACM conference on health, inference, and learning, 121130 (2020).
Huang, Y. & Chung, A. C. Disease prediction with edge-variational graph convolutional networks. Med. Image Anal. 77, 102375 (2022).
Article PubMed Google Scholar
Loftus, T. J. et al. Uncertainty-aware deep learning in healthcare: A scoping review. PLOS Digit. Health 1, e0000085 (2022).
Article PubMed PubMed Central Google Scholar
Abdar, M. et al. A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion 76, 243297 (2021).
Article Google Scholar
Abdar, M., Khosravi, A., Islam, S. M. S., Acharya, U. R. & Vasilakos, A. V. The need for quantification of uncertainty in artificial intelligence for clinical data analysis: Increasing the level of trust in the decision-making process. IEEE Syst. Man Cybern. Magaz. 8, 2840 (2022).
Article Google Scholar
Guo, C., Pleiss, G., Sun, Y. & Weinberger, K. Q. On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning Vol. 70 (eds Precup, D. & Teh, Y. W.) 13211330 (PMLR, 2017).
Google Scholar
Pearce, T., Brintrup, A. & Zhu, J. Understanding softmax confidence and uncertainty. Preprint at arXiv:2106.04972 (2021).
Alarab, I., Prakoonwit, S. & Nacer, M. I. Illustrative discussion of mc-dropout in general dataset: Uncertainty estimation in bitcoin. Neural Process. Lett. 53, 10011011 (2021).
Article Google Scholar
Alarab, I. & Prakoonwit, S. Uncertainty estimation-based adversarial attacks: a viable approach for graph neural networks. Soft Computing 113 (2023).
Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In International Conference on Machine Learning (eds Gal, Y. & Ghahramani, Z.) 10501059 (PMLR, 2016).
Google Scholar
Singer, O. C. et al. Collateral vessels in proximal middle cerebral artery occlusion: The endostroke study. Radiology 274, 851858 (2015).
Article PubMed Google Scholar
Bang, O. Y. et al. Impact of collateral flow on tissue fate in acute Ischaemic stroke. J. Neurol. Neurosurg. Psychiatry 79, 625629 (2008).
Article CAS PubMed Google Scholar
Menon, B. K. et al. Assessment of leptomeningeal collaterals using dynamic ct angiography in patients with acute ischemic stroke. J. Cerebral Blood Flow Metabol. 33, 365371 (2013).
Article Google Scholar
Berkhemer, O. A. et al. Collateral status on baseline computed tomographic angiography and intra-arterial treatment effect in patients with proximal anterior circulation stroke. Stroke 47, 768776 (2016).
Article CAS PubMed Google Scholar
Menon, B. et al. Regional leptomeningeal score on ct angiography predicts clinical and imaging outcomes in patients with acute anterior circulation occlusions. Am. J. Neuroradiol. 32, 16401645 (2011).
Article CAS PubMed PubMed Central Google Scholar
Kucinski, T. et al. Collateral circulation is an independent radiological predictor of outcome after thrombolysis in acute ischaemic stroke. Neuroradiology 45, 1118 (2003).
Article CAS PubMed Google Scholar
Sheth, S. A. et al. Collateral flow as causative of good outcomes in endovascular stroke therapy. J. Neurointerv. Surg. 8, 27 (2016).
Article PubMed Google Scholar
Seyman, E. et al. The collateral circulation determines cortical infarct volume in anterior circulation ischemic stroke. BMC Neurol. 16, 19 (2016).
Article Google Scholar
Elijovich, L. et al. Cta collateral score predicts infarct volume and clinical outcome after endovascular therapy for acute ischemic stroke: a retrospective chart review. J. Neurointerv. Surg. 8, 559562 (2016).
Article PubMed Google Scholar
Prasetya, H. et al. Value of ct perfusion for collateral status assessment in patients with acute ischemic stroke. Diagnostics 12, 3014 (2022).
Article PubMed PubMed Central Google Scholar
Potreck, A. et al. Rapid ct perfusion-based relative cbf identifies good collateral status better than hypoperfusion intensity ratio, cbv-index, and time-to-maximum in anterior circulation stroke. Am. J. Neuroradiol. 43, 960965 (2022).
Article CAS PubMed PubMed Central Google Scholar
Olivot, J. M. et al. Hypoperfusion intensity ratio predicts infarct progression and functional outcome in the defuse 2 cohort. Stroke 45, 10181023 (2014).
Article PubMed PubMed Central Google Scholar
Li, B.-H. et al. Cerebral blood volume index may be a predictor of independent outcome of thrombectomy in stroke patients with low aspects. J. Clin. Neurosci. 103, 188192 (2022).
Article ADS PubMed Google Scholar
Laredo, C. et al. Clinical and therapeutic variables may influence the association between infarct core predicted by ct perfusion and clinical outcome in acute stroke. Eur. Radiol. 32, 45104520 (2022).
Article CAS PubMed Google Scholar
Christodoulou, E. et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J. Clin. Epidemiol. 110, 1222 (2019).
Article PubMed Google Scholar
Ramos, L. A. et al. Predicting poor outcome before endovascular treatment in patients with acute ischemic stroke. Front. Neurol. 11, 580957 (2020).
Article PubMed PubMed Central Google Scholar
Leker, R. R. et al. Post-stroke aspects predicts outcome after thrombectomy. Neuroradiology 63, 769775 (2021).
Article PubMed Google Scholar
Peng, H., Long, F. & Ding, C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 12261238 (2005).
Article PubMed Google Scholar
Zhao, Z., Anand, R. & Wang, M. Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform. In 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (eds Zhao, Z. et al.) 442452 (IEEE, 2019).
Chapter Google Scholar
Paszke, A. et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems Vol. 32 (eds Paszke, A. et al.) 80248035 (Curran Associates, Inc., 2019).
- Machine learning and generative AI: What are they good for in 2025? - MIT Sloan - June 4th, 2025 [June 4th, 2025]
- Researchers use machine learning to improve gene therapy - Stanford Report - June 4th, 2025 [June 4th, 2025]
- Machine learning for workpiece mass prediction using real and synthetic acoustic data - Nature - June 4th, 2025 [June 4th, 2025]
- Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Input Representations Matter - Apple Machine Learning Research - June 4th, 2025 [June 4th, 2025]
- Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury - Nature - June 4th, 2025 [June 4th, 2025]
- A machine learning approach to carbon emissions prediction of the top eleven emitters by 2030 and their prospects for meeting Paris agreement targets... - June 4th, 2025 [June 4th, 2025]
- Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance - Nature - June 4th, 2025 [June 4th, 2025]
- Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique -... - June 4th, 2025 [June 4th, 2025]
- Your DNA Is a Machine Learning Model: Its Already Out There - Towards Data Science - June 4th, 2025 [June 4th, 2025]
- Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning... - June 4th, 2025 [June 4th, 2025]
- Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app - Nature - June 4th, 2025 [June 4th, 2025]
- How Machine Learning and Cascade Learning Open Doors of Advanced Automation - Supply & Demand Chain Executive - June 4th, 2025 [June 4th, 2025]
- New Hydrogenation Reaction Mechanism for Superhydride Revealed by Machine Learning - Asia Research News | - June 4th, 2025 [June 4th, 2025]
- AI experiences rapid adoption, but with mixed outcomes Highlights from VotE: AI & Machine Learning - S&P Global - June 4th, 2025 [June 4th, 2025]
- IIPE introduces online M.Tech in Data Science and Machine Learning for working professionals - India Today - June 4th, 2025 [June 4th, 2025]
- Introducing Windows ML: The future of machine learning development on Windows - Windows Blog - May 19th, 2025 [May 19th, 2025]
- Settlement strategies and their driving mechanisms of Neolithic settlements using machine learning approaches: a case study in Zhejiang Province -... - May 19th, 2025 [May 19th, 2025]
- MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning - Nature - May 19th, 2025 [May 19th, 2025]
- Leveraging stacking machine learning models and optimization for improved cyberattack detection - Nature - May 19th, 2025 [May 19th, 2025]
- Predicting land suitability for wheat and barley crops using machine learning techniques - Nature - May 10th, 2025 [May 10th, 2025]
- AI and Machine Learning - Ribeiro Preto adopts Optibus to optimise public bus system - Smart Cities World - May 10th, 2025 [May 10th, 2025]
- Childrens Hospital Los Angeles Leads Development of First Machine Learning Tool to Predict Risk of Cisplatin-Induced Hearing Loss - Business Wire - May 10th, 2025 [May 10th, 2025]
- Google is using machine learning to help Android users avoid unwanted and dangerous notifications - BetaNews - May 10th, 2025 [May 10th, 2025]
- London School of Emerging Technology (LSET) Concludes International Workshop on Emerging AI & Machine Learning Innovation - Barchart.com - May 10th, 2025 [May 10th, 2025]
- Thermal performance, entropy generation, and machine learning insights of AlO-TiO hybrid nanofluids in turbulent flow - Nature - May 10th, 2025 [May 10th, 2025]
- Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning - Nature - May 10th, 2025 [May 10th, 2025]
- How AI and machine learning are supercharging video conferencing tools - European CEO - May 10th, 2025 [May 10th, 2025]
- The need for a risk-based approach to AI and machine learning in healthcare - Health Tech World - May 10th, 2025 [May 10th, 2025]
- Integrated bioinformatics, machine learning, and molecular docking reveal crosstalk genes and potential drugs between periodontitis and systemic lupus... - May 10th, 2025 [May 10th, 2025]
- Adversarial Machine Learning in Detecting Inauthentic Behavior on Social Platforms - AiThority - May 10th, 2025 [May 10th, 2025]
- Exploring crop health and its associations with fungal soil microbiome composition using machine learning applied to remote sensing data - Nature - May 10th, 2025 [May 10th, 2025]
- Trust-based model and machine learning improve forest fire detection system - International Fire & Safety Journal - May 10th, 2025 [May 10th, 2025]
- A machine learning engineer shares the rsums that landed her jobs at Meta and X and what she'd change if she applied again - Business Insider Africa - May 5th, 2025 [May 5th, 2025]
- Recentive Analytics v. Fox: The Federal Circuit Provides Analysis on the Patent Eligibility of Machine Learning Claims - Mintz - May 5th, 2025 [May 5th, 2025]
- A machine learning engineer shares the rsums that landed her jobs at Meta and X and what she'd change if she applied again - Business Insider - May 5th, 2025 [May 5th, 2025]
- Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function... - May 5th, 2025 [May 5th, 2025]
- MicroAlgo Inc. Develops Classifier Auto-Optimization Technology Based on Variational Quantum Algorithms, Accelerating the Advancement of Quantum... - May 5th, 2025 [May 5th, 2025]
- Enhanced metal ion adsorption using ZnO-MXene nanocomposites with machine learning-based performance prediction - Nature - May 5th, 2025 [May 5th, 2025]
- Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births - BMC Pregnancy and Childbirth - May 5th, 2025 [May 5th, 2025]
- Machine learning provide new insights into how the brain responds to heroin use - News-Medical - May 2nd, 2025 [May 2nd, 2025]
- Machine Learning and AI in Basic HIV Research: From Big Data Analysis to Large Language Models - UNC Gillings School of Global Public Health - May 2nd, 2025 [May 2nd, 2025]
- Machine learning brings new insights to cells role in addiction, relapse - University of Cincinnati - May 2nd, 2025 [May 2nd, 2025]
- UH/UC Researchers Use Machine Learning to Map Brain Changes from Heroin Addiction - University of Houston - May 2nd, 2025 [May 2nd, 2025]
- Machine Learning Algorithm Predicts Shiba Inu Price In May You Should See This - The Crypto Update - May 2nd, 2025 [May 2nd, 2025]
- Seerist partners with SOCOM to enhance AI and machine learning for special operations - Defence Industry Europe - May 2nd, 2025 [May 2nd, 2025]
- How machine learning can spark many discoveries in science and medicine - The Indian Express - April 30th, 2025 [April 30th, 2025]
- Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar - Nature - April 30th, 2025 [April 30th, 2025]
- Gene Therapy Research Roundup: Gene Circuits and Controlling Capsids With Machine Learning - themedicinemaker.com - April 30th, 2025 [April 30th, 2025]
- Seerist and SOCOM Enter Five-Year CRADA to Advance AI and Machine Learning for Operations - PRWeb - April 30th, 2025 [April 30th, 2025]
- Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs - Nature - April 30th, 2025 [April 30th, 2025]
- Machine learning-based quantification and separation of emissions and meteorological effects on PM - Nature - April 30th, 2025 [April 30th, 2025]
- Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic... - April 30th, 2025 [April 30th, 2025]
- AQR Bets on Machine Learning as Asness Becomes AI Believer - Bloomberg.com - April 30th, 2025 [April 30th, 2025]
- Darktrace enhances Cyber AI Analyst with advanced machine learning for improved threat investigations - Industrial Cyber - April 21st, 2025 [April 21st, 2025]
- Infrared spectroscopy with machine learning detects early wood coating deterioration - Phys.org - April 21st, 2025 [April 21st, 2025]
- A simulation-driven computational framework for adaptive energy-efficient optimization in machine learning-based intrusion detection systems - Nature - April 21st, 2025 [April 21st, 2025]
- Machine learning model to predict the fitness of AAV capsids for gene therapy - EurekAlert! - April 21st, 2025 [April 21st, 2025]
- An integrated approach of feature selection and machine learning for early detection of breast cancer - Nature - April 21st, 2025 [April 21st, 2025]
- Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined... - April 21st, 2025 [April 21st, 2025]
- Autolomous CEO Discusses AI and Machine Learning Applications in Pharmaceutical Development and Manufacturing with Pharmaceutical Technology -... - April 21st, 2025 [April 21st, 2025]
- Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression - ACS Publications - April 21st, 2025 [April 21st, 2025]
- Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in... - April 21st, 2025 [April 21st, 2025]
- Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG... - April 12th, 2025 [April 12th, 2025]
- Increasing load factor in logistics and evaluating shipment performance with machine learning methods: A case from the automotive industry - Nature - April 12th, 2025 [April 12th, 2025]
- Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system -... - April 12th, 2025 [April 12th, 2025]
- Do LLMs Know Internally When They Follow Instructions? - Apple Machine Learning Research - April 12th, 2025 [April 12th, 2025]
- Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction - Nature - April 12th, 2025 [April 12th, 2025]
- Analysis and validation of hub genes for atherosclerosis and AIDS and immune infiltration characteristics based on bioinformatics and machine learning... - April 12th, 2025 [April 12th, 2025]
- AI and Machine Learning - Bentley and Google partner to improve asset analytics - Smart Cities World - April 12th, 2025 [April 12th, 2025]
- Where to find the next Earth: Machine learning accelerates the search for habitable planets - Phys.org - April 10th, 2025 [April 10th, 2025]
- Concurrent spin squeezing and field tracking with machine learning - Nature - April 10th, 2025 [April 10th, 2025]
- This AI Paper Introduces a Machine Learning Framework to Estimate the Inference Budget for Self-Consistency and GenRMs (Generative Reward Models) -... - April 10th, 2025 [April 10th, 2025]
- UCI researchers study use of machine learning to improve stroke diagnosis, access to timely treatment - UCI Health - April 10th, 2025 [April 10th, 2025]
- Assessing dengue forecasting methods: a comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil - Tropical... - April 10th, 2025 [April 10th, 2025]
- Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases - Nature - April 10th, 2025 [April 10th, 2025]
- How AI, Data Science, And Machine Learning Are Shaping The Future - Forbes - April 10th, 2025 [April 10th, 2025]
- Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer... - April 10th, 2025 [April 10th, 2025]
- From fax machines to machine learning: The fight for efficiency - HME News - April 10th, 2025 [April 10th, 2025]
- Carbon market and emission reduction: evidence from evolutionary game and machine learning - Nature - April 10th, 2025 [April 10th, 2025]
- Infleqtion Unveils Contextual Machine Learning (CML) at GTC 2025, Powering AI Breakthroughs with NVIDIA CUDA-Q and Quantum-Inspired Algorithms - Yahoo... - March 22nd, 2025 [March 22nd, 2025]