Early Detection of Traumatic Brain Hematoma Using Machine Learning-Powered Near-Infrared Spectroscopy: A Case Report...

Area of Science:

• Neuroscience
• Sports Medicine
• Biomedical Engineering

Background:

• Traumatic brain injuries, particularly concussions, are prevalent in contact sports.
• Kabaddi poses a high risk for head injuries, with limited research on acute neurological effects.
• Current gold-standard hematoma detection (CT scans) is often unavailable in field settings.

Purpose of the Study:

• To report the first use of machine learning-powered near-infrared spectroscopy (mNIRS) for detecting traumatic brain hematoma.
• To evaluate mNIRS as a rapid, noninvasive screening tool for on-field assessment in sports.
• To highlight the potential of mNIRS in managing acute head injuries in athletes.

Main Methods:

• A 13-year-old Kabaddi player sustained a head injury during a match.
• Machine learning-powered near-infrared spectroscopy (mNIRS) was used for initial assessment.
• Computed tomography (CT) was employed for confirmation of findings.

Main Results:

• mNIRS indicated a suspected traumatic brain hematoma post-injury.
• CT confirmed a right frontoparietal subdural hematoma with a 6-mm midline shift.
• A left temporal bone fracture was also identified.

Conclusions:

• mNIRS demonstrated effectiveness as a rapid, noninvasive screening tool for on-field traumatic brain hematoma detection.
• This case highlights the potential clinical utility of mNIRS in sports medicine.
• Further research is needed to optimize mNIRS for integration into sports medicine protocols.