Multimodal Artificial Intelligence for Detecting the Psychological State of Cancer Patients

NCT07751757 | Enrolling By Invitation DMC

• 2 other identifiers: AIPSY82030047
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 1

Brief Summary

Artificial intelligence (AI) technology is expected to assist clinical doctors in promptly identifying cancer patients at risk of developing psychological issues and to develop preemptive management plans, thereby enhancing their quality of life. Computer vision technology can directly capture and extract subtle changes in skin color from facial images in videos, assess heart rate using signal processing algorithms, and also extract facial expressions to evaluate psychological conditions through facial expression change signal processing algorithms. The accuracy rate can exceed 88%. By leveraging the capabilities of computer vision technology, it can accurately capture subtle movements and expressions of the human body, thereby understanding the internal psychological state and obtaining relevant psychological information

Conditions

Neoplasm

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operating Characteristic Curve (AUC) of the Multimodal Machine Learning Model for Anxiety and Depression Screening

  The AUC quantifies the overall discriminative ability of the final multimodal machine learning model to distinguish between patients with positive vs. negative anxiety/depression status. The AUC will be calculated on an independent test set that is strictly separated from the training and validation sets and will not be used in any model training or hyperparameter tuning.

  Data collected at two time points: 1 day pre-operatively and at ≤7 days post-operatively or at discharge, whichever came first

Eligibility Criteria

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

Study Population

Patients with cancers such as liver cancer, thyroid cancer and lung cancer et al. The demographic and clinical characteristics of tumor patients were collected and included, including age, gender, educational background, monthly income, marital status, ECOG score, liver function stage, Barcelona stage, recurrence frequency, lesion information, biochemical indicators, etc

Sponsors & Collaborators

• Chinese PLA General Hospital: lead

Study Sites (1)

Chinese PLA Hospital

Beijing, China

Location

MeSH Terms

Conditions

Neoplasms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator, Chief Physician

Study Record Dates

• First Submitted: March 25, 2026
• First Posted: August 7, 2026
• Study Start: March 1, 2021
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): December 31, 2028
• Last Updated: August 7, 2026

Record last verified: 2026-08

Locations

CN (1)