J Korean Neurosurg Soc.  2024 Sep;67(5):493-509. 10.3340/jkns.2023.0195.

Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future

• Kim KA
• Kim H
• Ha EJ
• Yoon BC
• Kim DJ

• KMID: 2558675
• DOI: http://doi.org/10.3340/jkns.2023.0195

Abstract

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

Keyword

Artificial intelligence; Clinical decision support; Critical care; Machine learning; Intensive care units; Traumatic brain injury

Figure

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  Fig. 1. A visual representation of the entire process of the ICP related event prediction model. A : Signal data such as ABP, ICP, and ECG, and EHR data such as personal information, medical history, and radiology reports are collected from patients during their hospitalization. B : Before using the collected data as model input, preprocessing is required. Signal data is filtered according to predefined ranges and segmented into windows of specific sizes. Additionally, considering the quality of signal, noise is removed. Non-signal data from patients undergoes processes such as encoding, normalization, and imputation of missing data, depending on the type of data. C : An appropriate model is selected and trained using the processed ICP-related signal parameters and patient health-related parameters. D : This model, by utilizing signal data from specific window and preprocessed EHR data, can predict life-threatening clinical conditions, such as IH, within a defined time interval. E : In a clinical setting, such predictions can assist clinicians in making decisions for early interventions like drug treatment, surgery, and supportive care. ABP : arterial blood pressure, ICP : intracranial pressure, ECG : electrocardiogram, EHRs : electronic health records, BMI : body mass index, CT : computed tomography, MRI : magnetic resonance imaging, LightGBM : light gradient boosting machine, LSTM : long short term memory, IH : intracranial hypertension, EVD : external ventricular drainage, DC : decompressive craniectomy.

Reference

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