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  • 1Computer Science, Duke University, Durham, NC.

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An interpretable deep-learning system significantly improved electroencephalography (EEG) interpretation accuracy for critical care clinicians. This AI tool enhances diagnostic capabilities and understanding of brain activity patterns in intensive care units (ICUs).

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Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Neuroscience
  • Clinical Neurophysiology

Background:

  • Intensive care units (ICUs) use electroencephalography (EEG) to monitor critically ill patients for brain injury.
  • Current EEG interpretation faces challenges due to limited clinician availability and subjective analysis, leading to variability.
  • Black-box deep-learning models lack transparency, hindering trust and clinical adoption despite potential benefits.

Purpose of the Study:

  • To develop an interpretable deep-learning system for classifying six key EEG patterns.
  • To provide case-based explanations for AI predictions to enhance clinician trust and understanding.
  • To evaluate the system's impact on diagnostic accuracy and its support for the ictal-interictal injury continuum hypothesis.

Main Methods:

  • Developed an interpretable deep-learning model trained on 50,697 EEG samples from 2711 ICU patients.
  • Classified six EEG patterns: seizure, LPDs, GPDs, LRDA, GRDA, and other, with expert-annotated data.
  • Evaluated AI assistance by comparing medical professionals' diagnostic accuracy with and without the system; assessed interpretability using neighborhood agreement statistics and latent space visualization.

Main Results:

  • AI assistance significantly improved mean user diagnostic accuracy from 47% to 71% (P<0.04).
  • The model achieved high Area Under the Receiver Operating Characteristic Curves (AUROCs) for all classes, outperforming a black-box model (P<0.0001).
  • Latent space visualization supported the ictal-interictal injury continuum hypothesis, revealing relationships between EEG patterns.

Conclusions:

  • The interpretable deep-learning model significantly enhances clinicians' EEG pattern classification accuracy.
  • The system's interpretable design fosters human-AI collaboration, potentially improving ICU diagnosis and patient care.
  • The model offers insights into EEG patterns and supports the ictal-interictal injury continuum hypothesis.