Importance Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Shared Decision Making and Communication.Scientific Discovery and the Future of Medicine.Health Care Economics, Insurance, Payment.Clinical Implications of Basic Neuroscience.Challenges in Clinical Electrocardiography.Installed Packages in the Dev Environment Results on Previously Published Dataset of 60 EEGsĮAppendix 1. Demographic Distribution of Patients in the Multicenter Test Dataset of EEGsĮTable 7. Training and Experience of the Human Experts Who Rated the Multicenter Test Dataset Of EEGsĮTable 6. Raw Figures of Epileptiform Findings in Previously Published Dataset of 60 EEGsĮTable 5. Performance of the Final Model on the Training Dataset.ĮTable 4. Threshold for Optimal Accuracy Based on Training Dataset (Calculated Using Balanced Bootstrap Resampling)ĮTable 3. A Cross Validation Scheme, Used for Model Development, Partitioning the Development Dataset Into Training and Validation DatasetsĮTable 2. Area Under the ROC Curve (AUC) Depending on the Duration of the EEG RecordingĮTable 1. autoSCORE: Integration of SCORE-AI With the Natus NeuroWorks EEG ReaderĮFigure 8. Approximate Mapping of the Model Output vs the Estimated Probability of the ConditionĮFigure 5. Receiver Operating Characteristics (ROC) and ROC Area Under the Curve (AUC) for the Entire Development SetĮFigure 4. Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) on the Cross-Validation DatasetsĮFigure 3. The Flow Diagram of the AI Model (SCORE-AI) Training and EvaluationĮFigure 2.
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