Construction of AI Model for Precision Imaging Diagnosis of Cranial Diseases
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Diagnostic accuracy of AI model (sensitivity, specificity, AUC)
研究概览
简要总结
The goal of this observational study is to develop and validate a high-precision AI diagnostic model for cranial diseases by integrating clinical knowledge systems (pathophysiological classification, age stratification, and anatomical localization) to simulate radiologists' diagnostic thinking. The main question it aims to answer is: Does the AI model improve diagnostic accuracy and consistency across different hospital levels, physician qualifications, and clinical scenarios compared to traditional diagnosis? Participants' cranial MRI data (including T1, T2, FLAIR, DWI sequences) and clinical information will be collected retrospectively (2015-2025) and prospectively (2026) to train and validate the model, which will be evaluated through performance metrics (accuracy, sensitivity, specificity) and clinical efficacy assessments (doctor vs. model, with/without model assistance). This study will establish a new paradigm for clinical AI implementation, providing methodological support for precision diagnosis of neurological diseases.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients clinically diagnosed with one of the following 48 cranial diseases: glioma, lymphoma, meningioma, pituitary macroadenoma, ependymoma, choroid plexus papilloma, schwannoma, medulloblastoma, metastatic tumor, chordoma, craniopharyngioma, germinoma, hemangioblastoma, cholesteatoma, teratoma, viral encephalitis, brain abscess, cerebral tuberculosis infection, cryptococcal encephalitis, cerebral cysticercosis, general soft meningitis (including bacterial, fungal infections, and autoimmune meningitis), tuberculous meningitis, ischemic stroke, cerebral venous sinus thrombosis, arteriovenous malformation, cavernous hemangioma, venous developmental malformation, aneurysm, cerebral small vessel disease, epidural hemorrhage, subdural hemorrhage, intracerebral hemorrhage, cerebral contusion, subarachnoid hemorrhage, diffuse axonal injury, multiple sclerosis, hippocampal sclerosis, focal cortical dysplasia, cortical or cerebral fissure malformations, basilar invagination, Chiari malformation, Dandy-Walker malformation, Rathke's cleft cyst, pituitary hypoplasia, adrenoleukodystrophy, Alzheimer's disease, Parkinson's disease, arachnoid cyst.
排除标准
- •Patients with other severe neurological diseases not included in the 48 specified cranial diseases; Patients unable to provide complete cranial imaging data (e.g., missing images or images of poor quality); Patients with severe cognitive impairment unable to cooperate with the study (e.g., unable to understand study procedures or communicate with site personnel); Pregnant or lactating women; Patients with severe dysfunction of major organs (e.g., heart, liver, kidney) that cannot tolerate study-related examinations; Patients who refuse to sign the informed consent form.
研究组 & 干预措施
Retrospective data collection from January 1, 2015 to December 31, 2025, including 70,000 subjects
including 70,000 unsupervised training cases and 2,000 supervised training cases for model development.
Prospective Cohort
Prospective data collection from March 1, 2026 to December 31, 2026, including 1,000 cases for model validation and clinical efficacy evaluation.
结局指标
主要结局
Diagnostic accuracy of AI model (sensitivity, specificity, AUC)
时间窗: Within 1 month of image acquisition
The primary outcome is the diagnostic performance of the AI model, including sensitivity, specificity, and area under the ROC curve (AUC), compared to the gold standard (e.g., histopathology or clinical diagnosis).
次要结局
未报告次要终点