Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
AI is advancing in EEG interpretation, potentially enhancing diagnostic capabilities and reducing expert workload, but further clinical validation is needed.
Where it sits
this study against the rest of the hmg (human menopausal gonadotropin) corpusSummary and findings
The review discusses the application of artificial intelligence models for the automated and semi-automated analysis of clinical electroencephalography (EEG). It highlights AI's potential to augment human expertise in EEG interpretation, particularly in spike and seizure detection and data analysis from wearables and critically ill patients. The review summarizes recent research and development in AI-based EEG interpretation.
Abstract
Electroencephalography is the most commonly used diagnostic tool for epilepsy. However, interpreting electroencephalograms (EEGs) requires expertise that is not widely available. Advances in digital technology and wearables have enabled large-scale EEG recording, generating vast amounts of data that cannot be managed through traditional visual interpretation by experts. Artificial intelligence (AI) has the potential to augment human expertise and reduce workloads. The application of artificial neural networks in analysing clinical EEG recordings has led to major breakthroughs, bringing AI-based EEG interpretation closer to clinical implementation. In this Review, we summarise the most important research and development results in this field from a clinical perspective. We provide an overview of AI applications in spike and seizure detection; analysis of data from wearable electroencephalographs, patients who are critically ill, and epilepsy surgery; and the automated interpretation of clinical EEGs.
Background
Electroencephalography (EEG) is a critical diagnostic tool for epilepsy, but its interpretation requires specialized expertise that is not universally accessible. With the advent of digital technology and wearable devices, EEG data collection has expanded significantly, necessitating new methods for data interpretation. This review addresses the potential of artificial intelligence (AI) to enhance EEG analysis, a topic of growing importance given the increasing volume of EEG data.
Methods
Not reported in abstract.
Results
The review highlights the application of artificial neural networks in EEG analysis, noting significant advancements in AI-based interpretation. It discusses AI's role in detecting spikes and seizures, analyzing data from wearable devices, and interpreting EEGs from critically ill patients and those undergoing epilepsy surgery. The review emphasizes AI's progress towards clinical implementation.
Interpretation
The review suggests that AI models have made significant progress in EEG interpretation, potentially reducing the workload on human experts. However, the lack of specific quantitative data in the review limits the ability to assess the clinical significance of these advancements. The findings align with existing literature on AI's potential in medical diagnostics but require further validation through clinical trials.
Key findings
- AI models can augment human expertise in EEG interpretation.
- AI applications include spike and seizure detection.
- AI is used in analyzing data from wearable EEG devices.
- AI aids in the interpretation of EEGs from critically ill patients.
- AI is moving towards clinical implementation for EEG analysis.
Limitations
- No new experimental data presented.
- Lacks specific quantitative findings.
- Focuses on summarizing existing research.
- No clinical trial data included.