NeoplasmAIPSYNCT07751757
Multimodal Artificial Intelligence for Detecting the Psychological State of Cancer Patients
An observational study of Neoplasm, run by Chinese PLA General Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Neoplasm
- How many people
- 1,500 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2021to 31 Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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
Who may be able to join
- 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
Who cannot join
- Age ≤18 years old
- Not diagnosed as a tumor patient
- Lack of autonomy and inability to conduct cooperative research
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
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
- Medical subject headings
- Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Data collected at two time points: 1 day pre-operatively and at ≤7 days post-operatively or at discharge, whichever came first
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Aug 2026
- Study start1 Mar 2021
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for NeoplasmStudies of the same condition, open and under way.
- More trials run by Chinese PLA General HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese PLA General Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ping Liang, Chinese PLA General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07751757
- Sponsor's protocol ID
- AIPSY
- Other IDs
- 82030047
- Acronym
- AIPSY
- Status last verified
- Aug 2026
- First submitted
- 25 Mar 2026
- Last updated
- 14 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 14 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Artificial Intelligence for Detecting the Psychological State of Cancer Patients
- ClinicalTrials.gov ID
- NCT07751757
- Acronym / sponsor ID
- AIPSY / AIPSY
- Conditions
- Neoplasm
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Chinese PLA General HospitalUniversity, hospital or other
- Study start
- 1 Mar 2021 (actual)
- Primary completion
- 31 Dec 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 7 Aug 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (3 listed)
- Age ≤18 years old
- Not diagnosed as a tumor patient
- Lack of autonomy and inability to conduct cooperative research
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area Under the Receiver Operating Characteristic Curve (AUC) of the Multimodal Machine Learning Model for Anxiety and Depression Screening
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Aug 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Neoplasm, run by Chinese PLA General Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 14 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operating Characteristic Curve (AUC) of the Multimodal Machine Learning Model for Anxiety and Depression Screening (time frame: Data collected at two time points: 1 day pre-operatively and at ≤7 days post-operatively or at discharge, whichever came first). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Aug 2026.