Neoplasm
Conditions
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
Interventions
None listed
Sponsors
Chinese PLA General Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age ≥18 years old * Patients with tumors diagnosed by magnetic resonance imaging or contrast-enhanced ultrasound * The patient has self-awareness and is able to cooperate with the research * Patients who voluntarily undergo psychological assessment tests
Exclusion criteria
* Age ≤18 years old * Not diagnosed as a tumor patient * Lack of autonomy and inability to conduct cooperative research
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Receiver Operating Characteristic Curve (AUC) of the Multimodal Machine Learning Model for Anxiety and Depression Screening | Data collected at two time points: 1 day pre-operatively and at ≤7 days post-operatively or at discharge, whichever came first | 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. |
Countries
China
Outcome results
None listed