Application of Machine Learning in the Diagnosis and Prognosis of Mild Traumatic Brain Injury Using Diffusion Tensor...

Traumatic Brain Injury (TBI) is a global health concern, with mild TBI (mTBI) being the most common form. Despite its prevalence, accurately diagnosing mTBI remains a significant challenge. While advanced neuroimaging techniques like diffusion tensor imaging (DTI) offer promise for more robust diagnosis, their clinical application is limited by inconsistent and heterogeneous post-injury findings. Recently, machine learning (ML) techniques, utilizing DTI metrics as features, have shown increasing utility in mTBI research. This approach helps identify distinct between-group features, paving the way for more precise and efficient diagnostic and prognostic tools.

This review aims to analyze studies employing ML techniques to assess changes in DTI metrics after mTBI.

Systematic review.

We conducted a systematic review, adhering to PRISMA guidelines, on the application of ML with DTI for mTBI diagnosis and prognosis on human subjects. This review identified 36 articles.

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Study quality was assessed using the Modified QualSyst Assessment Tool.

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The review found ML techniques using DTI Metrics either alone or in combination with other modalities (i.e., structural MRI, functional MRI, clinical scores, or demographics) can effectively classify mTBI patients from controls. These approaches have also demonstrated potential in classifying mTBI patients according to the degree of recovery and symptom severity. In addition, these ML models showed strong predictive power toward cognitive scores and brain structural decline, as quantified by brain-predicted age difference.

Larger, externally validated studies are needed to develop robust models for the diagnosis and prognosis of mTBI, using imaging biomarkers (including DTI) in conjunction with non-imaging, on-field, or clinical data. Despite the high predictive performance of ML algorithms, the clinical application remains distant, likely due to the small sample size of studies and lack of external validation, which raises concerns about overfitting.

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Stage 1.