Artificial Intelligence /
Machine Learning
Curated resources on AI and MA in neuromonitoring
Browse webinars, interviews, and publications exploring how AI and MA are being applied to brain monitoring, from signal analysis to clinical decision support.
Webinars
A selection of expert-led sessions from the Case Studies in Neurocritical Care AI and Brain Matters webinar series, focusing on machine learning models, signal processing, and the integration of AI into multimodal monitoring workflows to support more data-driven approaches to patient care.
Watch webinars and presentations to learn how clinicians and researchers are using AI and physiologic data to improve interpretation, decision-making, and the understanding of brain injury and recovery.
Browse all webinars to explore the full library across brain monitoring topics.
Automated Artifact Detection for ABP Data From Heuristics to Deep Neural Networks
Tony Okeke explores innovative approaches to identifying artifacts in arterial blood pressure (ABP) data using a variety of machine learning techniques.
NCC Big Data Analysis: Is Unsupervised Clustering a Solution?
Jeanette Tas provides an overview of how unsupervised clustering, which uncovers new data patterns, has been applied to the field of neurocritical care.
A Data-Driven Disease Course of TBI: Mining Prognostic Trajectories from ICU Data
Shubhayu speaks on integrating heterogeneous medical record data to model how clinical course influences 6-month GOSE outcomes. Using a prospective TBI cohort, he trains recurrent neural networks to predict ordinal GOSE scores every 2 hours from a token-embedded time series, incorporating missing values. His approach converts large patient records into interpretable time series with minimal processing.
Data Exploration through Multimodal Animation
Dr. Ramon Diaz-Arrastia speaks on his experience as a leader in TBI research and how analytics and big data can advance his work into the future.
Structuring Unstructured Data
Professor Ed Kim from Drexel University’s College of Computing and Informatics brings us up to speed on the background to modern AI, machine learning, and deep learning methods.
The Groundwork for Multimodal Machine Learning
Professor Ed Kim from Drexel University’s College of Computing and Informatics brings us up to speed on the background to modern AI, machine learning, and deep learning methods.
Precision Care After Cardiac Arrest
Jonathan Elmer discusses limitations in current neuro-prognostication tools, post-arrest prognostication and precision medicine, and high resolution multimodal data for precision care after cardiac arrest – the PRECICECAP study.
Improving Physiologic Threshold Compliance in Neurocritical Care
Gregory Hawryluk discusses concerns with poor threshold compliance in neurocritical care, the rationale for improving physiologic threshold compliance, and appreciation of a tool developed to improve physiologic threshold compliance and evidence supporting its effectiveness.
Promoting Bedside Precision Care in the ICU
Brian Appavu and Brandon Foreman explore recent work describing the interpretation and reporting of MMM (Multimodal Monitoring) data in the pediatric ICU and its impact on care, the value of interpreting and reporting MMM data to clinical teams, the practical aspects of reporting MMM data in clinical practice, and future directions to enhance the utility of MMM to improve patient care.
Conquering the Ictal-Interictal-Injury-Continuum: Using Big EEG Data and AI to automate and increase the value of intensive care brain monitoring
Brandon Westover and Sahar Zafar discuss using Ictal-Interictal Continuum Abnormality (IICA) burden to predict patient outcome, automating detection of seizure and IICA, and areas of the model needing improvement.
The Neuro ICU Cockpit
This month’s webinar takes you to Zurich, Switzerland where you will hear about the Neuro ICU Cockpit developed by Dr. Emanuela Keller together with ETH Zurich and IBM Research Zurich. Their ICU Cockpit is a great example of how to use comprehensive data for predicting status and optimizing patient management.
Interpreting and Reporting Multimodal Neuromonitoring Data: A Medical Record for the Brain
Dr. Brandon Foreman, MD, FACNS demonstrates how he records multimodal data and uses this new “medical record for the brain” in the management of his patients.
Interviews & Podcasts
Conversations with clinicians, data scientists, and researchers discussing how AI and machine learning are contributing to more data-driven care, including challenges in clinical adoption, workflow integration, and translating models into real-world practice.
Listen to expert perspectives and practical discussions on how physiologic data and AI tools are being used to support research, interpretation, and patient management.
Visit the full Interviews & Podcasts page to discover more perspectives from across the field.
Predictive Modeling in TBI: Insights from Florian van Leeuwen
Florian is a PhD Candidate in Statistics at Utrecht University, focusing on prediction models for complex datasets and applying these models to cases of brain injury. In this interview, he sits down with Dick to discuss what he believes should be the future of neuroscience with specific regards to brain injury and neurocritical care. The two speak on how prediction models can assist clinicians in making difficult decisions regarding individual TBI patients, and how we can make models output more reliable, precise predictions from complex multimodal data sources.
Big Data and the Cloud in TBI with Craig Maddux & Mark Talens
Craig Maddux, healthcare analytics and technical specialist at IBM, and Mark Talens, technical executive for data and AI in healthcare, discuss the roles of big data, the cloud, and AI in the care of patients with traumatic brain injury.Â
Publications
A curated collection of peer-reviewed research on AI and machine learning in neurocritical care, including algorithm development, validation studies, and clinical translation focused on advancing data-driven care. Publications are organized by the newsletter editions in which they were featured, making it easy to explore them in context.
Read recent studies and publications to stay informed on emerging models, data-driven methods, and ongoing advances in AI-enabled brain monitoring research.
Explore all publications to stay up to date with the latest findings in the field.
April/May 2026
Okeke, T., Shrestha, M., […], Elmer, J. (2026). Evaluating artifact detection algorithms for the arterial blood pressure waveform acquired from the intensive care unit: A PRECICECAP informatics approach. Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2026.110434.
Strelzyk, F. (2026). The Critical Role of Training Data. Zeto. https://zeto-inc.com/blog/the-critical-role-of-training-data/.
March 2026
Freyer, O., Mathias, R., […], Gilbert, S. (2026). The regulation of artificial intelligence in intensive care units: from narrow tools to generalist systems. npj Digit. Med. https://doi.org/10.1038/s41746-026-02535-3.
Martin, J., Afshar, M., […], Churpek, M., (2026). Explainable multimodal deep learning models for variable-length sequences in critically ill patients. Journal of Biomedical Informatics. https://doi.org/10.1016/j.jbi.2026.105001.
February 2026
Azarfar, G., Naimimohasses, S., […], Bhat, M. (2025). Responsible adoption of multimodal artificial intelligence in health care: promises and challenges. The Lancet Digital Health. https://doi.org/10.1016/j.landig.2025.100917.
Doan, L.M.T., Shahhosseini, […], Occhipinti, A. (2026). Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging. Commun Eng. https://doi.org/10.1038/s44172-026-00602-x.
Njei, B., Al-Ajlouni, Y. A., […], Al-Ajlouni, A. F. (2026). Artificial intelligence agents in healthcare research: A scoping review. PloS One. https://doi.org/10.1371/journal.pone.0342182.
November 2025
Wu, Jingyi, Bai, Shaojie, […], Kainerstorfer, Jana. Synthetic-data-driven LSTM framework for tracing cardiac pulsation in optical signals. Biomedical Optics Express. https://doi.org/10.1364/BOE.574286.
May 2025
Chen, X., Olakorede, I., Bögli, S., […], Smielewski, P. (2025) Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series, arXiv. https://doi.org/10.48550/arXiv.2504.21209.
December 2024
Liu, T., Hetherington, T.C., […], Cleveland, J.A. Does AI-Powered Clinical Documentation Enhance Clinician Efficiency? A Longitudinal Study. NEJM AI 1(12). https://doi.org/10.1056/AIoa2400659.
Sharma, R., Salman, S., Gu, Q., Freeman, W.D. (2024). Advancing Neurocritical Care with Artificial Intelligence and Machine Learning: The Promise, Practicalities, and Pitfalls Ahead. Neurol Clin 43(1):153-165. https://doi.org/10.1016/j.ncl.2024.08.003.
November 2024
Jiang, M., Cao, F., Zhang, Q. et al. (2024). Model-Predicted Brain Temperature Computational Imaging by Multimodal Noninvasive Functional Neuromonitoring of Cerebral Oxygen Metabolism and Hemodynamics: MRI-Derived and Clinical Validation. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678×241270485.
September 2024
Bhattacharyay, S. (2024). From Big Data to Personal Narratives: A Supervised Learning Framework of Decoding the Course of Traumatic Brain Injury in Intensive Care. Apollo – University of Cambridge Repository. https://doi.org/10.17863/CAM.109007.
July 2024
Smielewski, P., Beqiri, E., Mataczynski, C., Placek, M., Kazimierska, A., Hutchinson, P.J., Czosnyka, M., Kasprowicz, M. (2024). Advanced Neuromonitoring Powered by ICM+ and its Place in the Brand New AI World, Reflections at the 20th Anniversary Boundary. Brain and Spine. https://doi.org/10.1016/j.bas.2024.102835.
Srichawla, B.S. (2024). Future of Neurocritical Care: Integrating Neurophysics, Multimodal Monitoring, and Machine Learning. World Journal of Critical Care Medicine. https://doi.org/10.5492/wjccm.v13.i2.91397
Vakitbilir, N., Bergmann, T., Froese, L., Gomez, A., Sainbhi, A.S., Stein, K.Y., Islam, A., Zeiler, F.A. (2024). Multivariate Modeling and Prediction of Cerebral Physiology in Acute Traumatic Neural Injury: A Scoping Review. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2024.108766
March 2024
Ravindranath, M., Candan, K.S., Sapino, M.L., Appavu, B. (2024). MMA: metadata supported multi-variate attention for onset detection and prediction. Data Mining and Knowledge Discovery. DOI: 10.1007/s10618-024-01008-z
October 2023
Beqiri E, Badjatia N, […], Curing Coma Campaign and its Contributing Members. (2023). Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Physiology and Big Data. Neurocrit Care. 2023 Sep 13. Epub ahead of print. PMID: 37704934. https://doi.org/10.1007/s12028-023-01846-7.
July 2023
Zou, B., Mi, X., Stone, E., & Zou, F. (2023). A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/s12911-023-02155-x.
May 2023
Zou, B., Mi, X., Stone, E., & Zou, F. (2023). A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/s12911-023-02155-x
October 2022
Ack, S. E., Loiseau, S. Y., Sharma, G., Goldstein, J. N., Lissak, I. A., Duffy, S. M., … & Rosenthal, E. S. (2022). Neurocritical Care Performance Measures Derived from Electronic Health Record Data are Feasible and Reveal Site-Specific Variation: A CHoRUS Pilot Project. Big Data in Neurocritical Care, Neurocritical Care, 37(2), 276-290. https://pubmed.ncbi.nlm.nih.gov/35689135/.
Elmer, J., He, Z., May, T., Osborn, E., Moberg, R., Kemp, S., … & Hirsch, K. G. (2022). Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach. Big Data in Neurocritical Care, Neurocritical Care, 1-11. https://pubmed.ncbi.nlm.nih.gov/35229231/.
Moberg, R., Moyer, E. J., Olson, D., Rosenthal, E., & Foreman, B. (2022). Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward. Big Data in Neurocritical Care, Neurocritical Care, 1-4. https://pubmed.ncbi.nlm.nih.gov/35641807/.
Brain Physics Lectures
The Brain Physics Lectures provide important background for understanding how computational methods, signal analysis, and advanced monitoring technologies are applied in modern neurocritical care. Developed by Prof. Marek Czosnyka and Prof. Peter Smielewski at the University of Cambridge, the series includes lectures focused on brain signal analysis, physiologic modeling, and the foundations behind platforms such as ICM+.
For clinicians, engineers, and researchers interested in data-driven neurocritical care, these lectures offer valuable insight into the physiologic and analytical principles that support emerging AI and machine learning applications in neuromonitoring.
Visit the Brain Physics Lecture page to browse the full lecture series.
Lecture 27: ICM+ - Software for Brain Monitoring
Dr Peter Smielewski talks about the past and present of ICM+, the multimodal, high resolution, data collection, integration and analysis clinical research tool, highlighting some of its more important features and common uses.
Lecture 28: Overview of Techniques for Brain Signal Analysis
Dr Peter Smielewski (Department of Clinical Neurosciences, University of Cambridge) talks about a spectrum of analytical approaches to extracting clinically valuable information from waveforms captured in a neuro-ICU from bed-side monitors.