Development and Validation of Delirium Recognition Using Computer Vision in Neuro-critical Patients

NCT07136207 | Recruiting

• 1 other identifier: PX2023021
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Delirium; Artificial Intelligence (AI)

Keywords

Delirium; Artificial Intelligence (AI); neurocritical care units; Facial recognition

Outcome Measures

Primary Outcomes (3)

• Accuracy of the delirium prediction model

  The accuracy of the delirium prediction model will be calculated as the proportion of correct predictions among total predictions.

  Through study completion, an average of 1 year

• Sensitivity of the delirium prediction model

  Sensitivity (true positive rate) will be assessed as the proportion of actual delirium cases correctly identified by the model.

  Through study completion, an average of 1 year

• Specificity of the delirium prediction model

  Specificity (true negative rate) will be calculated as the proportion of non-delirium cases correctly identified by the model.

  Through study completion, an average of 1 year

Secondary Outcomes (2)

• F1 Score of the delirium prediction model

  Through study completion, an average of 1 year

• AUC of the facial feature curve for delirium patients

  Through study completion, an average of 1 year

Study Arms (2)

Neurocritical non-delirium patients

For neurocritical non-delirium patients, the investigators record facial expression videos, which are used during model development to compare with the facial expressions of delirium patients.

Neurocritical delirium patients

The investigators record facial expression videos of neurocritical delirium patients and perform frame sampling on the videos to analyze and extract the facial expression features specific to delirium. Based on this analysis, the investigators develop a model for delirium recognition in neurocritical patients.

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This study selects neurocritical patients as the population and collects facial expression data from both delirium and non-delirium patients.

You may qualify if:

• Neurocritical patients admitted to the ICU, including postoperative neurosurgical patients, stroke patients, and those receiving ICU care due to other neurological conditions.
• Age over 18 years.
• Signed informed consent.

You may not qualify if:

• Age under 18 years.
• Persistent coma (GCS ≤ 8) within 7 days pre- and post-surgery, making delirium assessment impossible.
• Did not survive more than 24 hours in the ICU.
• Patients with facial paralysis, post-traumatic facial disfigurement, or other conditions that could significantly affect facial recognition.

Sponsors & Collaborators

• Beijing Tiantan Hospital: lead
• Affiliated Jinyang Hospital of Guizhou Medical University: collaborator
• Affiliated Hospital of Guizhou Medical University: collaborator

Study Sites (1)

Beijing Tiantan Hospital

Beijing, Beijing Municipality, 100000, China

RECRUITING

Related Publications (10)

• Heintz TA, Badathala A, Wooten A, Cu CW, Wallace A, Pham B, Wallace AW, Cobert J. Preliminary Development and Validation of Automated Nociception Recognition Using Computer Vision in Perioperative Patients. Anesthesiology. 2025 Apr 1;142(4):726-737. doi: 10.1097/ALN.0000000000005370. Epub 2025 Jan 13.

  PMID: 39804295 BACKGROUND

• Atee M, Hoti K, Parsons R, Hughes JD. A novel pain assessment tool incorporating automated facial analysis: interrater reliability in advanced dementia. Clin Interv Aging. 2018 Jul 16;13:1245-1258. doi: 10.2147/CIA.S168024. eCollection 2018.

  PMID: 30038491 BACKGROUND

• Goldberg TE, Chen C, Wang Y, Jung E, Swanson A, Ing C, Garcia PS, Whittington RA, Moitra V. Association of Delirium With Long-term Cognitive Decline: A Meta-analysis. JAMA Neurol. 2020 Nov 1;77(11):1373-1381. doi: 10.1001/jamaneurol.2020.2273.

  PMID: 32658246 BACKGROUND

• Aldecoa C, Bettelli G, Bilotta F, Sanders RD, Audisio R, Borozdina A, Cherubini A, Jones C, Kehlet H, MacLullich A, Radtke F, Riese F, Slooter AJ, Veyckemans F, Kramer S, Neuner B, Weiss B, Spies CD. European Society of Anaesthesiology evidence-based and consensus-based guideline on postoperative delirium. Eur J Anaesthesiol. 2017 Apr;34(4):192-214. doi: 10.1097/EJA.0000000000000594.

  PMID: 28187050 BACKGROUND

• Ely EW, Margolin R, Francis J, May L, Truman B, Dittus R, Speroff T, Gautam S, Bernard GR, Inouye SK. Evaluation of delirium in critically ill patients: validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Crit Care Med. 2001 Jul;29(7):1370-9. doi: 10.1097/00003246-200107000-00012.

  PMID: 11445689 BACKGROUND

• Ahmed A, Garcia-Agundez A, Petrovic I, Radaei F, Fife J, Zhou J, Karas H, Moody S, Drake J, Jones RN, Eickhoff C, Reznik ME. Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage. Front Neurol. 2023 Jun 9;14:1135472. doi: 10.3389/fneur.2023.1135472. eCollection 2023.

  PMID: 37360342 BACKGROUND

• Al-Hindawi A, Vizcaychipi M, Demiris Y. A Dual-Camera Eye-Tracking Platform for Rapid Real-Time Diagnosis of Acute Delirium: A Pilot Study. IEEE J Transl Eng Health Med. 2024 May 7;12:488-498. doi: 10.1109/JTEHM.2024.3397737. eCollection 2024.

  PMID: 39050621 BACKGROUND

• Oh J, Cho D, Park J, Na SH, Kim J, Heo J, Shin CS, Kim JJ, Park JY, Lee B. Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning. Physiol Meas. 2018 Mar 27;39(3):035004. doi: 10.1088/1361-6579/aaab07.

  PMID: 29376502 BACKGROUND

• Eeles E, Tronstad O, Teodorczuk A, Flaws D, Fraser JF, Dissanayaka N. Face and content validity of a mobile delirium screening tool adapted for use in the medical setting (eDIS-MED): Welcome to the machine. Australas J Ageing. 2024 Jun;43(2):415-419. doi: 10.1111/ajag.13288. Epub 2024 Feb 28.

  PMID: 38415380 BACKGROUND

• Nejati V, Khorrami AS, Fonoudi M. Neuromodulation of facial emotion recognition in health and disease: A systematic review. Neurophysiol Clin. 2022 Jun;52(3):183-201. doi: 10.1016/j.neucli.2022.03.005. Epub 2022 Apr 12.

  PMID: 35428551 BACKGROUND

MeSH Terms

Conditions

Delirium

Condition Hierarchy (Ancestors)

Confusion; Neurobehavioral Manifestations; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Neurocognitive Disorders; Mental Disorders

Central Study Contacts

Huang Huawei, Doctoral degree

CONTACT

+8613599058877; huanghw0403@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 18, 2025
• First Posted: August 22, 2025
• Study Start: August 30, 2025
• Primary Completion: December 30, 2025
• Study Completion: January 30, 2026
• Last Updated: August 22, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)