機械学習を活用した近赤外分光法による外傷性脳内血腫の早期検出：カバディ選手における症例報告

Traumatic brain injury is a significant concern in contact sports, with concussions being the most common type. Kabaddi, a high-contact sport, carries a high risk of head injuries, yet limited research has addressed their acute neurological impact. Conventional imaging such as computed tomography is the gold standard for hematoma detection but is often inaccessible in field settings. This case report describes the first use of machine learning-powered near-infrared spectroscopy (mm) to detect a traumatic brain hematoma in a 13-year-old Kabaddi player. After a head injury, near-infrared spectroscopy indicated a suspected hematoma, which was confirmed by computed tomography showing a right frontoparietal subdural hematoma with a 6-mm midline shift and a left temporal bone fracture. mNIRS proved to be a rapid, noninvasive screening tool for on-field assessment. Further research is warranted to optimize its clinical utility and integration into sports medicine protocols.