Revolutionizing dementia detection: Leveraging vision and Swin transformers for early diagnosis

Area of Science:

• Neurology
• Artificial Intelligence
• Medical Imaging

Background:

• Dementia is a growing global health concern, with cases projected to triple by 2050.
• Cognitive decline associated with dementia significantly impairs daily functioning and decision-making.
• Early detection is vital for effective management and intervention strategies.

Purpose of the Study:

• To explore the potential of advanced AI, specifically computer vision and deep learning, for early dementia detection.
• To evaluate the efficacy of different AI algorithms in identifying early signs of dementia from brain imaging data.

Main Methods:

• Utilized computer vision and deep learning algorithms to analyze medical imaging data, such as positron emission tomography (PET) scans.
• Compared the performance of Swin transformer, vision transformer, and convolutional neural network algorithms in detecting subtle brain changes indicative of dementia.

Main Results:

• The Swin transformer algorithm exhibited superior overall accuracy in detecting dementia compared to other tested algorithms.
• AI analysis of imaging data successfully identified subtle structural brain changes, facilitating earlier diagnosis.

Conclusions:

• Early dementia detection is essential for proactive management, personalized care, and implementing preventive measures.
• AI technologies, particularly the Swin transformer, offer a promising avenue for improving the accuracy and timeliness of dementia diagnosis.
• Advancements in AI can significantly enhance patient outcomes and reduce the healthcare burden associated with dementia.