[Artificial Intelligence for Diagnostic Support of Neuroimage]

Area of Science:

• Neuroimaging
• Artificial Intelligence
• Machine Learning

Background:

• Neuroimaging plays a crucial role in diagnosing neurological disorders.
• Artificial intelligence (AI) offers promising advancements in diagnostic support for neuroimaging.
• Accurate and early diagnosis of Alzheimer's disease (AD) is critical for patient management.

Purpose of the Study:

• To develop and evaluate an AI-based software for predicting Alzheimer's disease (AD) using 3D brain MR images.
• To compare the diagnostic accuracy of the AI model against conventional statistical analysis methods.
• To assess the potential clinical utility of AI in neuroimaging-based AD diagnosis.

Main Methods:

• Development of a software utilizing support vector machines (SVM) for AD prediction.
• Training the SVM model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
• Validation of the AI model on independent datasets: Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) and Japanese ADNI.

Main Results:

• The AI software demonstrated high accuracy in predicting Alzheimer's disease (AD).
• AI-based prediction showed superior performance compared to conventional statistical analysis.
• The model's effectiveness was validated across diverse international cohorts.

Conclusions:

• AI, specifically SVM on 3D brain MR images, offers a highly accurate method for predicting AD.
• The developed AI tool shows significant potential for clinical diagnostic support in neuroimaging.
• This approach represents a promising advancement in the early detection and management of Alzheimer's disease.