This research project employs machine learning algorithms integrated with computer vision, image processing, and pattern recognition technologies to perform digital analysis of facial expression behaviors in neurocritical care patients with delirium. By constructing multidimensional high-level features of delirium, the investigators have established a classification model based on behavioral. The primary objective of this study is to address the critical challenge of achieving precise and efficient delirium diagnosis in neurologically critically ill patients through automated facial expression behavior recognition.
Eligibility
Sex
ALL
Min age
18 Years
Max age
80 Years
Healthy volunteers
No
Inclusion Criteria:
1. Neurocritical patients admitted to the ICU, including postoperative neurosurgical patients, stroke patients, and those receiving ICU care due to other neurological conditions.
2. Age over 18 years.
3. Signed informed consent.
Exclusion Criteria:
1. Age under 18 years.
2. Persistent coma (GCS ≤ 8) within 7 days pre- and post-surgery, making delirium assessment impossible.
3. Did not survive more than 24 hours in the ICU.
4. Patients with facial paralysis, post-traumatic facial disfigurement, or other conditions that could significantly affect facial recognition.
5. Exclusion of patients with severe dementia, Parkinson's disease, depression, or other conditions that might impact facial emotional expressions.
Primary outcome measure(s)
Accuracy of the delirium prediction model — Through study completion, an average of 1 year The accuracy of the delirium prediction model will be calculated as the proportion of correct predictions among total predictions.
Sensitivity of the delirium prediction model — Through study completion, an average of 1 year Sensitivity (true positive rate) will be assessed as the proportion of actual delirium cases correctly identified by the model.
Specificity of the delirium prediction model — Through study completion, an average of 1 year Specificity (true negative rate) will be calculated as the proportion of non-delirium cases correctly identified by the model.
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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