An observational study in Neoplasm, sponsored by Chinese PLA General Hospital. Enrolling by invitation at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-11-13.
Sponsored by Chinese PLA General Hospital · Observational
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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
10,822 studies on the registry are indexed under Neoplasms; 2,324 are open to participants now.
This study's planned enrollment of 1,500 is above the median of 200 across 2,641 observational studies indexed under Neoplasms.
Browse Neoplasms studies →Chinese PLA General Hospital is the lead sponsor of 571 studies on the registry; 207 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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
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Exclusion Criteria:
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.
Time frame: Data collected at two time points: 1 day pre-operatively and at ≤7 days post-operatively or at discharge, whichever came first
Plan to share: Undecided
No publications or documents are linked to this record.
Started Mar 2021 (actual). Primary completion is estimated for Dec 2028, 93 months after the start.
Of 1,098 non-industry observational studies under Neoplasms, half reached primary completion within 61 months of starting (the median), counting those still running; a quarter within 24 and three quarters within 227. This study's planned 93 months is above that.
Counting every sponsor, half of 1,246 studies reached it within 58 months; a quarter within 24 and three quarters within 210.
Neoplasms covers many different conditions, not one, so the studies compared here vary widely. Read these figures as a rough guide.
How these figures are worked out, and which studies are left out of them.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
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No contact was published for this record. The registry link below has the sponsor’s details.
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