Artificial intelligence for traumatic brain injury imaging: a translational review from algorithm development to...

Area of Science:

• Neuroimaging
• Medical Artificial Intelligence
• Radiology

Background:

• Traumatic brain injury (TBI) poses a significant global health challenge.
• Computed tomography (CT) is the primary acute imaging tool for TBI.
• CT interpretation faces limitations in objectivity, efficiency, and prognostic accuracy.

Purpose of the Study:

• To review the development and clinical application of AI in TBI neuroimaging.
• To assess AI's performance in detecting, classifying, and segmenting TBI-related abnormalities.
• To evaluate AI's potential in predicting patient outcomes and serving as surrogate endpoints.

Main Methods:

• Systematic synthesis of AI applications in TBI imaging.
• Review of deep learning models, particularly convolutional neural networks.
• Analysis of multimodal data fusion for outcome prediction.

Main Results:

• AI models achieve high sensitivity (up to 96%) in identifying intracranial hemorrhage.
• AI automates lesion segmentation and radiological scoring.
• AI shows promise in predicting mortality and functional recovery, but clinical validation is ongoing.

Conclusions:

• AI offers transformative potential for objective, efficient, and precise TBI neuroimaging.
• Clinical translation requires addressing data heterogeneity, interpretability, and workflow integration.
• Future research must focus on multi-center validation and patient outcome-focused trials.