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.
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 Type
OBSERVATIONAL
Enrollment
200
Baseline transcranial sonography assessment of brain structural imaging features, including substantia nigra echogenicity, raphe nuclei echogenicity, and third-ventricle width.
Taizhou Second People's Hospital
Taizhou, Zhejiang, China
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.
Time frame: Baseline; analyzed after completion of baseline data collection
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.
Time frame: 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.
Time frame: 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.
Time frame: Baseline; analyzed after completion of baseline data collection
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