SchizophreniaTCS-ML-SZNCT07604129
Transcranial Sonography and Machine Learning for Schizophrenia Identification
An observational study of Schizophrenia, run by Taizhou Second People's Hospital at 1 site in China.
Trial summary
Schizophrenia is a serious mental illness. Doctors usually diagnose schizophrenia by talking with patients, reviewing symptoms, and using clinical assessment. In early or less typical cases, diagnosis may be difficult. Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Schizophrenia
- What is being tested?
- Transcranial Sonography
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 15 Jun 2026to 31 Dec 2027
- How long will I take part?
- 1 Day
- 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 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Adults aged 18 to 65 years will be recruited from Taizhou Second People's Hospital and related recruitment channels. The study population will include participants with schizophrenia diagnosed according to ICD-10 criteria and healthy control participants without a personal or family history of psychiatric disorders. Healthy controls will be matched as far as possible to the schizophrenia group by basic demographic characteristics. All participants will undergo baseline transcranial sonography assessment and clinical data collection.
Who may be able to join
- Schizophrenia group:
- Adults aged 18 to 65 years.
- Diagnosis of schizophrenia according to ICD-10 criteria by a psychiatrist.
- Able to complete clinical assessment and transcranial sonography examination.
- No other severe physical disease, neurological disease, or major psychiatric disorder.
- Written informed consent provided by the participant or legally authorized representative.
Show all 14Show fewer
- Healthy control group:
- Adults aged 18 to 65 years.
- No personal history of psychiatric disorders.
- No family history of psychiatric disorders.
- No severe physical disease, neurological disease, or major psychiatric disorder.
- Basic demographic characteristics matched as far as possible to the schizophrenia group.
- Able to complete clinical assessment and transcranial sonography examination.
- Written informed consent provided by the participant or legally authorized representative.
Who cannot join
- Severe physical disease or neurological disease.
- History of drug or alcohol abuse.
- Inability to complete clinical assessment or transcranial sonography examination.
- Inadequate temporal acoustic window or poor image quality preventing valid transcranial sonography measurements.
- Acute or clinically unstable state that prevents completion of study procedures.
- Comorbid major psychiatric disorder, such as major depressive disorder.
- Refusal or withdrawal of informed consent.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Taizhou Second People's Hospital
Taizhou, Zhejiang, 317200
Xiaochen Zhang, +8615967690053, 15967690053@163.com
About this study
Official title: Development and Validation of an Early Prediction Model for Schizophrenia Integrating Transcranial Sonography Structural Imaging and Machine Learning
Schizophrenia is a serious mental illness. Doctors usually diagnose schizophrenia by talking with patients, reviewing symptoms, and using clinical assessment. In early or less typical cases, diagnosis may be difficult.
This study will look at whether brain ultrasound information can help doctors identify features related to schizophrenia. The ultrasound scan used in this study is called transcranial sonography. It is a non-invasive scan that uses sound waves to look at brain structures through natural thin areas of the skull.
Read more
The study will include adults with schizophrenia and adults without a personal or family history of mental disorders. All participants will have a transcranial sonography scan and provide basic clinical information. The researchers will measure brain ultrasound features, including the substantia nigra, raphe nuclei, and third ventricle, and will combine these features with clinical information.
The main question is whether a computer model using ultrasound and clinical information can help distinguish adults with schizophrenia from adults without schizophrenia. The model is intended only as a research tool and possible future aid for doctors. It will not replace diagnosis by a psychiatrist and will not change the participant's usual medical care.
Read the detailed description
This is a prospective observational case-control study designed to develop and evaluate a machine-learning model for identifying schizophrenia using transcranial sonography (TCS) structural imaging features and clinical information.
Schizophrenia is clinically heterogeneous, and diagnosis depends mainly on clinical symptoms and psychiatric assessment. TCS is a non-invasive imaging method that can assess selected deep brain structures through the temporal acoustic window. Previous studies suggest that ultrasound features of structures such as the substantia nigra, raphe nuclei, and third ventricle may be related to neuropsychiatric disorders. This study will investigate whether TCS-derived structural imaging features, combined with clinical variables, can support auxiliary identification of schizophrenia.
Adults aged 18 to 65 years with schizophrenia diagnosed according to ICD-10 criteria and matched adults without a personal or family history of psychiatric disorders will be enrolled. The planned enrollment is 200 participants, including approximately 100 participants with schizophrenia and 100 healthy controls. Participants will undergo baseline TCS assessment and clinical data collection. No therapeutic intervention will be assigned by the investigators, and participation will not replace or alter usual clinical care.
TCS assessments will focus on selected brain structural imaging features, including substantia nigra echogenicity, raphe nuclei echogenicity, and third-ventricle width. Clinical information may include demographic characteristics, medical history, family history, disease course, medication history, and symptom assessment data when available. TCS measurements will be performed according to a standardized procedure, and image quality control will be conducted to reduce measurement variability.
The collected TCS and clinical variables will be integrated into a structured dataset for model development. Candidate machine-learning methods may include logistic regression, random forest, support vector machine, and XGBoost. Feature selection and model optimization will be performed within the model development process. Internal validation will be used to assess model performance, and additional independent data may be used for external validation if available.
Model performance will be evaluated using discrimination, calibration, and clinical utility metrics, including the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1 score, calibration assessment, and decision curve analysis where appropriate. Model interpretability will be explored using SHAP to assess the relative contribution of TCS imaging features and clinical variables.
The resulting model is intended as an auxiliary research tool for schizophrenia identification. It is not intended to make a definitive diagnosis, replace psychiatric assessment, or guide treatment decisions independently.
- Conditions
- Schizophrenia
- Being tested
- Transcranial Sonography
- Keywords
- Transcranial Sonography, Machine Learning, Diagnostic Model, Schizophrenia Identification, Substantia Nigra, Raphe Nuclei, Case-Control Study, Brain Ultrasound
- Medical subject headings
- Schizophrenia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Transcranial SonographyDiagnostic test
Baseline transcranial sonography assessment of brain structural imaging features, including substantia nigra echogenicity, raphe nuclei echogenicity, and third-ventricle width.
| Study group | Role | What they receive |
|---|---|---|
| Schizophrenia Group | Adults aged 18 to 65 years with schizophrenia diagnosed according to ICD-10 criteria. Participants will undergo baseline transcranial sonography assessment and clinical data collection. Receives: Diagnostic Test: Transcranial Sonography | |
| Healthy Control Group | Adults aged 18 to 65 years without a personal or family history of psychiatric disorders and matched as far as possible to the schizophrenia group by basic demographic characteristics. Participants will undergo baseline transcranial sonography assessment and clinical data collection. Receives: Diagnostic Test: Transcranial Sonography |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the ROC Curve of the Final TCS-Clinical Model
The area under the receiver operating characteristic curve will be used to assess the ability of the final machine-learning model, based on transcranial sonography and clinical variables, to distinguish participants with ICD-10 schizophrenia from healthy controls. The reference standard will be clinical diagnosis according to ICD-10 criteria.
Measured: Baseline; analyzed after completion of baseline data collection
Secondary outcomes (3)
Sensitivity and Specificity of the Final TCS-Clinical Model
Sensitivity and specificity will be calculated for the final model for distinguishing participants with schizophrenia from healthy controls, using a pre-specified or internally optimized classification threshold.
Measured: Baseline; analyzed after completion of baseline data collection
Other Classification Performance Metrics of the Final Model
Accuracy, precision, recall, and F1 score will be calculated to further evaluate the classification performance of the final model for distinguishing participants with schizophrenia from healthy controls.
Measured: Baseline; analyzed after completion of baseline data collection
Calibration Performance of the Final TCS-Clinical Model
Calibration will be evaluated by comparing predicted probabilities with observed diagnostic status using calibration plots, calibration slope, calibration intercept, and/or Brier score, as appropriate.
Measured: Baseline; analyzed after completion of baseline data collection
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2026
- Study start15 Jun 2026 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SchizophreniaStudies of the same condition, open and under way.
- 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
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 200 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Taizhou Second People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiaocheng Zhang, Taizhou Second People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07604129
- Sponsor's protocol ID
- 25YWB115
- Other IDs
- TZEY-EC-2026-05
- Acronym
- TCS-ML-SZ
- Status last verified
- May 2026
- First submitted
- 17 May 2026
- Last updated
- 22 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The individual participant data generated or analyzed during this study will not be publicly shared because of participant privacy, the sensitive nature of psychiatric clinical data, and restrictions in the ethics approval and informed consent. Aggregated results will be reported in the published article.
Study at a glance
From the registry record, last updated by the sponsor on 22 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an Early Prediction Model for Schizophrenia Integrating Transcranial Sonography Structural Imaging and Machine Learning
- ClinicalTrials.gov ID
- NCT07604129
- Acronym / sponsor ID
- TCS-ML-SZ / 25YWB115
- Conditions
- Schizophrenia
- Keywords
- Transcranial Sonography, Machine Learning, Diagnostic Model, Schizophrenia Identification, Substantia Nigra, Raphe Nuclei, Case-Control Study, Brain Ultrasound
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Taizhou Second People's HospitalUniversity, hospital or other
- Study start
- 15 Jun 2026 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 22 May 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.
Schizophrenia is a serious mental illness. Doctors usually diagnose schizophrenia by talking with patients, reviewing symptoms, and using clinical assessment. In early or less typical cases, diagnosis may be difficult.
This study will look at whether brain ultrasound information can help doctors identify features related to schizophrenia. The ultrasound scan used in this study is called transcranial sonography. It is a non-invasive scan that uses sound waves to look at brain structures through natural thin areas of the skull.
The study will include adults with schizophrenia and adults without a personal or family history of mental disorders. All participants will have a transcranial sonography scan and provide basic clinical information. The researchers will measure brain ultrasound features, including the substantia nigra, raphe nuclei, and third ventricle, and will combine these features with clinical information.
The main question is whether a computer model using ultrasound and clinical information can help distinguish adults with schizophrenia from adults without schizophrenia. The model is intended only as a research tool and possible future aid for doctors. It will not replace diagnosis by a psychiatrist and will not change the participant's usual medical care.
Detailed description
This is a prospective observational case-control study designed to develop and evaluate a machine-learning model for identifying schizophrenia using transcranial sonography (TCS) structural imaging features and clinical information.
Schizophrenia is clinically heterogeneous, and diagnosis depends mainly on clinical symptoms and psychiatric assessment. TCS is a non-invasive imaging method that can assess selected deep brain structures through the temporal acoustic window. Previous studies suggest that ultrasound features of structures such as the substantia nigra, raphe nuclei, and third ventricle may be related to neuropsychiatric disorders. This study will investigate whether TCS-derived structural imaging features, combined with clinical variables, can support auxiliary identification of schizophrenia.
Adults aged 18 to 65 years with schizophrenia diagnosed according to ICD-10 criteria and matched adults without a personal or family history of psychiatric disorders will be enrolled. The planned enrollment is 200 participants, including approximately 100 participants with schizophrenia and 100 healthy controls. Participants will undergo baseline TCS assessment and clinical data collection. No therapeutic intervention will be assigned by the investigators, and participation will not replace or alter usual clinical care.
TCS assessments will focus on selected brain structural imaging features, including substantia nigra echogenicity, raphe nuclei echogenicity, and third-ventricle width. Clinical information may include demographic characteristics, medical history, family history, disease course, medication history, and symptom assessment data when available. TCS measurements will be performed according to a standardized procedure, and image quality control will be conducted to reduce measurement variability.
The collected TCS and clinical variables will be integrated into a structured dataset for model development. Candidate machine-learning methods may include logistic regression, random forest, support vector machine, and XGBoost. Feature selection and model optimization will be performed within the model development process. Internal validation will be used to assess model performance, and additional independent data may be used for external validation if available.
Model performance will be evaluated using discrimination, calibration, and clinical utility metrics, including the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1 score, calibration assessment, and decision curve analysis where appropriate. Model interpretability will be explored using SHAP to assess the relative contribution of TCS imaging features and clinical variables.
The resulting model is intended as an auxiliary research tool for schizophrenia identification. It is not intended to make a definitive diagnosis, replace psychiatric assessment, or guide treatment decisions independently.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Target follow-up
- 1 Day
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Schizophrenia Group | – | Diagnostic Test: Transcranial SonographyAdults aged 18 to 65 years with schizophrenia diagnosed according to ICD-10 criteria. Participants will undergo baseline transcranial sonography assessment and clinical data collection. |
| Healthy Control Group | – | Diagnostic Test: Transcranial SonographyAdults aged 18 to 65 years without a personal or family history of psychiatric disorders and matched as far as possible to the schizophrenia group by basic demographic characteristics. Participants will undergo baseline transcranial sonography assessment and clinical data collection. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Adults aged 18 to 65 years will be recruited from Taizhou Second People's Hospital and related recruitment channels. The study population will include participants with schizophrenia diagnosed according to ICD-10 criteria and healthy control participants without a personal or family history of psychiatric disorders. Healthy controls will be matched as far as possible to the schizophrenia group by basic demographic characteristics. All participants will undergo baseline transcranial sonography assessment and clinical data collection.
Key inclusion criteria (14 listed)
- Adults aged 18 to 65 years.
- Diagnosis of schizophrenia according to ICD-10 criteria by a psychiatrist.
- Able to complete clinical assessment and transcranial sonography examination.
- No other severe physical disease, neurological disease, or major psychiatric disorder.
- Written informed consent provided by the participant or legally authorized representative.
Key exclusion criteria (7 listed)
- Severe physical disease or neurological disease.
- History of drug or alcohol abuse.
- Inability to complete clinical assessment or transcranial sonography examination.
- Inadequate temporal acoustic window or poor image quality preventing valid transcranial sonography measurements.
- Acute or clinically unstable state that prevents completion of study procedures.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Transcranial SonographyBaseline transcranial sonography assessment of brain structural imaging features, including substantia nigra echogenicity, raphe nuclei echogenicity, and third-ventricle width. | Diagnostic test | Healthy Control Group, Schizophrenia Group |
Primary outcome
Area Under the ROC Curve of the Final TCS-Clinical Model
Time frame: Baseline; analyzed after completion of baseline data collection
The area under the receiver operating characteristic curve will be used to assess the ability of the final machine-learning model, based on transcranial sonography and clinical variables, to distinguish participants with ICD-10 schizophrenia from healthy controls. The reference standard will be clinical diagnosis according to ICD-10 criteria.
3 secondary outcomes
Sensitivity and Specificity of the Final TCS-Clinical Model
Time frame: Baseline; analyzed after completion of baseline data collection
Other Classification Performance Metrics of the Final Model
Time frame: Baseline; analyzed after completion of baseline data collection
Calibration Performance of the Final TCS-Clinical Model
Time frame: Baseline; analyzed after completion of baseline data collection
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 22 May 2026.
- Individual participant dataSharing planned: No. The individual participant data generated or analyzed during this study will not be publicly shared because of participant privacy, the sensitive nature of psychiatric clinical data, and restrictions in the ethics approval and informed consent. Aggregated results will be reported in the published article.
Related trials for Schizophrenia
Not yet recruitingPhase 1
Phase I Trial of HHK101 in Patients With Schizophrenia
Schizophrenia
Not yet recruiting
Integrated Psychoeducation Program for Internalized Stigma in Schizophrenia
Schizophrenia, Schizoaffective Disorder
Not yet recruiting
Non-Invasive Vagus Nerve Stimulation as a Probe to Map Functional Connectivity in Schizophrenia Using Functional Magnetic Resonance Imaging
Schizophrenia Spectrum Disorders
Not yet recruiting
Reducing Stigma - A Treatment for Early Psychosis to Prevent Suicide
Suicide, Mental Health Stigma
RecruitingPhase 3
A Study to Evaluate KarXT as a Treatment for Psychosis Associated With Alzheimer's Disease (ADEPT-4)
Alzheimer Disease
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.
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 Schizophrenia, run by Taizhou Second People's 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 14 inclusion and 7 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: opening soon, as of the sponsor's last update on 22 May 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?
Contact Xiaochen Zhang (+8615967690053, 15967690053@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the ROC Curve of the Final TCS-Clinical Model (time frame: Baseline; analyzed after completion of baseline data collection). 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 22 May 2026.