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CLINICAL AND COMPUTED TOMOGRAPHY CHARACTERISTICS OF TRAUMATIC BRAIN INJURY PATIENTS WITH PROGNOSES ASSISTED BY ARTIFICIAL INTELLIGENCE
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Abstract
Objectives: Assessment some clinical and computed tomography (CT) features of patients with traumatic brain injury (TBI) and their prognoses predicted and patient data managed using artificial intelligence (AI)-based software. Methods: A prospective analysis was conducted on 75 patients diagnosed with TBI based on clinical and paraclinical evaluations at 103 Military Hospital from August 1, 2023, to April 1, 2024. Prognostic assessments were generated using AI software. Results: There were 3 recorded deaths (4%). Favorable recovery outcomes (GOS at 1 month) were observed in 93.3% of patients. The mean age was 43.22 ± 21.13 years, and 74.7% were male. The average Glasgow Coma Scale (GCS) score on admission was 13.45 ± 2.62. Hemiparesis was documented in 2.7% of cases. Subarachnoid hemorrhage (60%) and hemorrhagic contusions (48%) were the predominant types of lesions. The average midline shift measured 6.57 ± 1.9 mm, and obliteration of the basal cisterns was observed in 6 cases (8%). Conclusion: Patients whose prognostic evaluations and data management were facilitated by artificial intelligence systems showed favorable clinical outcomes, with high recovery rates and low mortality.
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Keywords
Machine learning; Artificial intelligence; Traumatic brain injury.
References
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