Artificial Intelligence Algorithm-Based MRI in Evaluating the Treatment Effect of Acute Cerebral Infarction

Area of Science:

• Medical Imaging
• Artificial Intelligence in Medicine
• Neurology

Background:

• Acute cerebral infarction diagnosis relies on timely and accurate imaging.
• Traditional imaging methods like CT have limitations in sensitivity and image quality.
• Exploring advanced AI algorithms for medical image analysis is crucial.

Purpose of the Study:

• To evaluate the application of artificial intelligence (AI) algorithm-based magnetic resonance imaging (MRI) for diagnosing acute cerebral infarction.
• To compare the diagnostic performance of AI-enhanced MRI with computed tomography (CT).
• To assess the effectiveness of AI in improving MRI image recognition and lesion evaluation.

Main Methods:

• Eighty patients with suspected acute cerebral infarction were included.
• MRI images were reconstructed using a deep dictionary learning algorithm.
• AI-enhanced MRI was compared with conventional CT imaging and other reconstruction methods (SAE, single-layer dictionary, KAVD).

Main Results:

• Deep dictionary learning significantly improved MRI image reconstruction compared to other methods.
• AI-based MRI demonstrated statistically significant improvements (P < 0.05) in lesion detection and diameter measurement compared to CT.
• AI-enhanced MRI showed higher sensitivity than CT, with only one misdiagnosis, leading to improved disease detection and image quality.

Conclusions:

• AI-based MRI, particularly using deep dictionary learning, is a highly sensitive and effective tool for diagnosing and evaluating acute cerebral infarction.
• This AI-driven approach offers superior image quality and diagnostic accuracy over CT.
• AI-enhanced MRI provides valuable clinical support for acute cerebral infarction management.