Integrating artificial intelligence (AI) with MRI for distinguishing Benign and Malignant tumors of Brain.
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- The primary outcome of this study is to accurately identify brain tumors and differentiate between benign and malignant types using AI-enhanced MRI analysis. This involves leveraging AI algorithms trained on diverse MRI sequences to provide precise and reliable classifications that align with histopathological findings, the gold standard for diagnosis.
研究概览
简要总结
The integration of artificial intelligence (AI) with magnetic resonance imaging (MRI) offers a groundbreaking approach to distinguishing benign and malignant brain tumors. This study focuses on developing and validating AI models using advanced machine learning and deep learning techniques to analyze MRI sequences like T1-weighted, T2-weighted, and contrast-enhanced images. A comprehensive dataset of tumor cases is utilized to ensure robust and accurate model training.Key outcomes include evaluating the diagnostic accuracy, sensitivity, and specificity of AI-enhanced MRI compared to conventional radiological methods. The study also emphasizes the interpretability of AI models to ensure clinical trust and explores their impact on improving diagnostic efficiency, reducing time, and aiding treatment planning. The research aims to advance radiological practices, offering precise, efficient, and reliable tools for brain tumor classification.
研究设计
- 研究类型
- Observational
入排标准
- 年龄范围
- 10.00 Year(s) 至 90.00 Year(s)（—）
- 性别
- All
入选标准
- •Population Criteria: Patients aged 10 years and above.
- •Individuals with histopathologically confirmed benign or malignant brain tumors.
- •Medical History: Documented clinical history relevant to brain tumor diagnosis and treatment.
- •Patients with no prior surgical, radiation, or chemotherapy intervention to ensure baseline imaging accuracy.
- •MRI Scan Data: High-quality MRI images, including T1-weighted, T2-weighted, and contrast-enhanced sequences.
- •Images free from significant artifacts that could compromise AI analysis.
排除标准
- •Population Criteria: Patients below 10 years of age.
- •Patients with non-brain tumors or non-tumor brain conditions (e.g., infections, vascular abnormalities).
- •Medical History: Patients with incomplete or unavailable clinical history.
- •Individuals who have undergone prior surgical, radiation, or chemotherapy interventions before MRI acquisition.
- •Patients with significant comorbidities or contraindications to MRI (e.g., pacemakers, metallic implants).
- •MRI Scan Data: Low-quality MRI images with significant artifacts or incomplete imaging sequences.
- •MRI scans missing critical sequences such as T1-weighted, T2-weighted, or contrast-enhanced images.
- •Imaging data not obtained from standardized MRI protocols.
结局指标
主要结局
The primary outcome of this study is to accurately identify brain tumors and differentiate between benign and malignant types using AI-enhanced MRI analysis. This involves leveraging AI algorithms trained on diverse MRI sequences to provide precise and reliable classifications that align with histopathological findings, the gold standard for diagnosis.
时间窗: 24 hours
次要结局
- The accuracy of AI-enhanced MRI in differentiating benign from malignant brain tumors, measured against histopathological findings as the gold standard.(30 days)
研究者
Pranathi Ravula
Saveetha Medical college and Hospital