Traumatic brain injury (TBI) affects 250 per 100,000 people worldwide and is linked to 30 to 50% of trauma-related mortality, disproportionately affecting adolescents, young adults, and the elderly, with mild TBI (mTBI)—80% of cases—resulting in significant long-term effects and remaining difficult to diagnose with computed tomography (CT) and magnetic resonance imaging (MRI).[1] Machine learning is advancing neuroimaging by enabling the detection and tracking of disease patterns, bridging imaging data with biological processes, and improving our understanding of brain variability and function.[2]
In a study by Vergara et al,[3] the researchers compared the resting state functional network connectivity (rsFNC) and diffusion MRI (dMRI) for detecting mTBI. The results showed that rsFNC achieved higher accuracy (84.1%) compared with dMRI (75.5%) and the combined approach (74.5%). Significant differences in rsFNC were observed in the cerebellum versus sensorimotor networks and the left angular gyrus versus precuneus in mTBI patients.[3] These findings suggest that rsFNC may provide viable biomarkers for mTBI detection, with cerebellar connectivity being a key area for further investigation.
Luo et al[4] concluded that combining multiple imaging parameters—amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), decompressive craniectomy (DC), voxel-mirrored homotopic connectivity (VMHC), and short-range local functional connectivity (FCD)—improves the classification of mTBI from normal controls, achieving an area under the curve (AUC) of 0.778, accuracy of 81.11%, sensitivity of 88%, and specificity of 75%. Key brain regions contributing to this classification include the bilateral cerebellum, left orbitofrontal cortex, left cuneus, left temporal pole, right inferior occipital cortex, bilateral parietal lobe, and left supplementary motor area. These multiparameter combinations enhance mTBI detection by targeting brain regions linked to emotion and cognition.[4]
Another study[5] proposed an automated approach for segmenting and assessing subdural hematomas (SDHs) in head CT scans, achieving a Dice similarity coefficient of 75.35 and 79.97% for moderate and severe SDHs. The model demonstrated high specificity (99.89%) for healthy individuals and outperformed deep learning models by integrating hand-crafted features with data-driven deep features. While the algorithm performed robustly for SDHs larger than 25 mL, challenges remain in detecting mild SDHs, chronic hematomas, and dealing with anisotropic image resolution.[5] The model's generalizability to mild SDHs and chronic hematomas, particularly in the elderly, should be improved by incorporating diverse datasets and optimizing resolution handling to enhance its clinical applicability.
Traumatic subdural hematoma (TSDH) accounts for approximately 40% of traumatic intracranial hematomas and is a leading cause of death from TBI. The increasing incidence from traffic and falls highlights the need for accurate diagnosis and hematoma volume assessment. The Tada formula, while correlating well with manual segmentation, becomes less accurate with larger volumes. Recent convolutional neural network (CNN) based algorithms outperform traditional methods, like the ABC/2 formula, in segmentation accuracy. The CNN algorithm for calculating TSDH volume in head CT scans shows good agreement with manual segmentation but requires further exploration due to limitations such as small sample size, single-center data, and some algorithmic errors.[6]
In another study,[7] the authors evaluated a CNN for detecting and quantifying SDH thickness, volume, and midline shift (MLS) from noncontrast head CT scans, finding high sensitivity (91.4%), specificity (96.4%), and accuracy (95.1%) in an independent validation set. The CNN showed excellent agreement with manual segmentation (Pearson's correlation: 0.97), with minimal errors in SDH thickness (2.75 mm) and MLS (0.93 mm).[7] Future work should expand the sample size, include multicenter data, and refine the algorithm to reduce segmentation errors and enhance its clinical applicability across diverse populations.
Another study[8] developed a prognostic model combining deep learning of head CT scans and clinical data to predict long-term outcomes after severe TBI (sTBI). The fusion model showed higher accuracy in predicting mortality (AUC: 0.92) and unfavorable outcomes (AUC: 0.88) compared with the International Mission for Prognosis And Clinical Trial (IMPACT) model in internal data, although it underperformed in external validation against the IMPACT model. The fusion model outperformed predictions made by neurosurgeons.[8] Future research should focus on refining the model for broader clinical applicability, enhancing performance in external cohorts, and incorporating additional patient factors for improved prognostic precision.
Khalili et al[9] developed machine learning models, including random forest (RF) and generalized linear model, to predict short- and long-term outcomes for TBI patients, identifying key prognostic factors such as pupil condition, Glasgow Coma Scale Motor score, cistern status, and patient age. The models demonstrated high performance but require further validation with larger, diverse datasets for reliable clinical application.[9] Future research should focus on expanding the dataset and investigating long-term patient conditions beyond mortality prediction.
An automated method for reliably calculating acute intracranial lesion volumes, cistern volumes, and MLS in noncontrast CT images of TBI patients called icobrain was presented in a study by Jain et al.[10] This method offers advantages in speed and consistency over manual and semiquantitative methods, with performance comparable to expert measurements, which has been validated using a multicenter dataset from the center for traumatic brain injury (CENTER-TBI) study. It demonstrates high accuracy in lesion and cistern volume calculations and MLS detection, making it a valuable tool for both clinical practice and large-scale patient studies. Future improvements aim to extend its capabilities to edema and different lesion types.[10]
In a cross-sectional study by Shafie et al,[11] the researchers explored the relationship between clinical presentations and brain CT findings in patients with mTBI. It identified that the presence of posttraumatic vomiting (PTV), posttraumatic amnesia (PTA), raccoon eyes, and a Glasgow Coma Scale (GCS) score of 13 or 14 were predictive factors for abnormal CT findings in mTBI patients. Additionally, a GCS of 15 was associated with a normal CT scan. The findings align with prior studies suggesting the importance of these factors in predicting brain injuries in mTBI cases.[11]
The rapid growth of multimodal data in health informatics has driven the adoption of machine learning, particularly deep learning, which leverages advancements in computational power and data storage. Deep learning has proven effective in various health informatics applications, including translational bioinformatics, medical imaging, and public health, by automatically generating optimized features and semantic interpretations. However, while it holds promise for reshaping artificial intelligence (AI) in health care, the technique also presents challenges, such as the need for large datasets and computational resources, which should be carefully considered in future research.[12]
A study by Matsuo et al[13] developed a three-class outcome prediction model for TBI using machine learning, incorporating clinical data from 1,200 patients across six Japanese hospitals. The XGBoost model demonstrated strong performance with 82.5% accuracy and an AUC of 0.901, outpacing logistic regression and neural network models, especially in predicting severe disability/vegetative states. While the model effectively predicted in-hospital outcomes, further work is needed to improve its generalization and mortality prediction, aiming to enhance patient stratification in TBI treatments.[13]
In another recent study,[14] the authors used machine learning to develop predictive models for long-term care needs in TBI patients, analyzing data from 2,020 ICU admissions. XGBoost with 27 features achieved the highest AUC (0.823), followed closely by RF (0.817), with both models providing valuable insights into patient outcomes. The study highlights the potential of these models to guide early planning for long-term care and resource allocation in TBI management.[14]
AI has shown promise in advancing the diagnosis, clinical management, and research of TBI, offering tools for triage, predicting outcomes, and classifying TBI phenotypic clusters. However, challenges such as standardizing data, avoiding overfitting, and ensuring ethical use remain critical. While AI can expedite clinical decisions, its integration into health care must prioritize transparency, patient-centered care, and adherence to ethical guidelines. As the field evolves, careful regulation and monitoring will be essential to ensure AI is applied responsibly in TBI management.[15]
Advancements in machine learning for TBI diagnosis, outcome prediction, and clinical decision-making hold significant promise in improving patient care. However, challenges remain, such as the need for better generalizability to mild SDHs, chronic hematomas, and diverse patient populations,[5]
[9] and addressing segmentation errors in volume assessments.[6]
[7] Future research should focus on refining algorithms, expanding datasets, and incorporating additional patient factors to enhance prognostic accuracy and clinical applicability.[3]
[7]
[8] While machine learning techniques show potential, they require large datasets and computational resources, which need to be carefully considered moving forward.[12] Finally, improving the model's performance in external cohorts and predicting long-term patient conditions beyond mortality should be prioritized for broader applicability.[13]
To conclude, machine learning can significantly enhance TBI care by improving diagnostic precision, prognostic accuracy, and treatment personalization. However, overcoming challenges like data quality, model transparency, and broad applicability will be essential to fully realize its benefits in clinical settings.
We hope that our valuable insights are taken into consideration in future research.
