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

TL;DR

This review explores how AI, especially deep learning, can improve the diagnosis and prognosis of traumatic brain injury through imaging, but highlights the need for more clinical validation.

Contribution

The paper provides a translational review of AI in TBI imaging, emphasizing the pathway from algorithm development to clinical implementation.

Findings

AI models, like convolutional neural networks, show high sensitivity (up to 96%) in detecting intracranial hemorrhage and segmenting lesions.

Multimodal AI approaches can predict patient outcomes, including mortality and functional recovery.

Despite improved diagnostic metrics, robust trials showing direct patient benefit are still lacking.

Abstract

Traumatic brain injury (TBI) remains a major global health burden, with computed tomography (CT) serving as the frontline imaging modality for acute assessment. However, CT interpretation is hampered by subjectivity, oversight in busy emergency settings, and limited prognostic accuracy of traditional scoring systems. Artificial intelligence (AI), particularly deep learning, offers transformative potential to automate and enhance TBI neuroimaging analysis.

This review systematically synthesizes the translational pathway of AI in TBI imaging, from algorithm development to clinical implementation. AI models, especially convolutional neural networks, demonstrate high performance (sensitivity up to 96%) in detecting and classifying intracranial hemorrhage, segmenting lesions, and automating radiological scoring. Through multimodal data fusion, AI further shows promise in predicting patient outcomes, from near-term mortality to long-term functional recovery. Beyond pattern recognition, AI-derived imaging biomarkers hold potential as surrogate endpoints in therapeutic trials. However, prospective and real-world validation studies reveal a critical evidence gap: while AI tools improve diagnostic metrics and workflow efficiency, robust randomized controlled trials demonstrating direct improvement in patient-centered outcomes are still lacking.

AI is poised to revolutionize TBI neuroimaging by increasing diagnostic objectivity, efficiency, and prognostic precision. Successful clinical translation, however, requires overcoming key challenges related to data heterogeneity, model interpretability, and workflow integration. Future efforts must prioritize the generation of high-quality multi-center datasets, the development of explainable AI, and—most critically—the execution of prospective trials with patient outcome endpoints. Collaborative, interdisciplinary research is essential to translate these technological advances into tangible improvements in TBI care and recovery.

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