Advanced neuromonitoring powered by ICM+ and its place in the Brand New AI World, reflections at the 20th anniversary boundary

• Published in: Brain & spine 2024, Vol.4, p.102835, Article 102835
• Main Authors: Smielewski, P., Beqiri, E., Mataczynski, C., Placek, M., Kazimierska, A., Hutchinson, P.J., Czosnyka, M., Kasprowicz, M.
• Format: Article
• Language: English
• Subjects: Advanced neuro-monitoring; ICM+ software; ICP pulse wave morphology; Intracranial pressure analysis; Pulse shape index; Traumatic brain injury
• ISSN: 2772-5294; 2772-5294
• DOI: 10.1016/j.bas.2024.102835

Adoption of the ICM+® brain monitoring software by clinical research centres worldwide has been continuously growing over the past 20 years. This has necessitated ongoing updates to accommodate evolving neuromonitoring research needs, including recent explosion of artificial intelligence (AI). We sought to provide an update on the current features of the software. In particular, we aimed to highlight the new options of integrating AI models. We reviewed all currently available ICM+ analytical areas and discussed potential AI based extensions in each. We tested a proof-of-concept integration of an AI model and evaluated its performance for real-time data processing. ICM+ current analytical tools serve both real-time (bed-side) and offline (file based) analysis, including the calculation engine, Signal Calculator, Custom Statistics, Batch tools, ScriptLab and charting. The ICM+ Python plugin engine allows to execute custom Python scripts and take advantage of complex AI frameworks. For the proof-of-concept, we used a neural network convolutional model with 207,000 trainable parameters that classifies morphology of intracranial pressure (ICP) pulse waveform into 5 pulse categories (normal to pathological plus artefactual). When evaluated within ICM+ plugin script on a Windows 10 laptop the classification of a 5 min ICP waveform segment took only 0.19s with a 2.3s of initial, one-off, model loading time required. Modernised ICM+ analytical tools, reviewed in this manuscript, include integration of custom AI models allowing them to be shared and run in real-time, facilitating rapid prototyping and validating of new AI ideas at the bed-side. •ICM+ is by now a mature clinical research platform in neuromonitoring.•ICM + allows bed-side data integration, real time analysis and visualisation.•All the computational areas within ICM + are now extendible via Python code plugins.•A proof-of-concept plugin implementing a deep learning model was successfully tested.