Automated Detection of Idiopathic Intracranial Hypertension Using Artificial Intelligence: An Approach Based on Magnetic Resonance Imaging and Magnetic Resonance Venography Data
Bratislava Medical Journal, cilt.127, sa.3, ss.1238-1248, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 127 Sayı: 3
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s44411-026-00504-6
- Dergi Adı: Bratislava Medical Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Ultimate (EBSCO)
- Sayfa Sayıları: ss.1238-1248
- Anahtar Kelimeler: Idiopathic intracranial hypertension, Artificial intelligence, Magnetic resonance imaging, Transverse sinus stenosis
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Purpose: Idiopathic intracranial hypertension (IIH) is a condition characterized by increased intracranial pressure without an identifiable cause. This study aimed to assess the efficacy of artificial intelligence (AI)-based algorithms in diagnosing IIH using magnetic resonance imaging (MRI) and magnetic resonance venography (MRV) data. Materials and Methods: A total of 194 individuals (74 IHH patients and 120 controls) were examined, and brain MRV and T2-weighted MRI images were analyzed. Two different models were trained using AI algorithms on MRV and T2 images, and these models were evaluated together. Results: The accuracy of the models was calculated to be 80.60% ± 9.17 using only MRV data, 79.86% ± 8.50 using only T2 data, and 82.6% ± 8.88 when both datasets were combined, and the area under the curve values were 84.97% ± 9.24, 85.46% ± 8.00, and 90.55% ± 8.53, respectively. Conclusion: The results indicate that AI algorithms offer high accuracy and reliability in diagnosing IIH. AI-assisted diagnostic methods can accelerate clinical processes and eliminate observer-related variations.