Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Machine Learning Models for Predicting Abnormal Brain CT Scan Findings in Mild Traumatic Brain Injury Patients.
0
Zitationen
17
Autoren
2025
Jahr
Abstract
XGBoost and Random Forest achieved high predictive accuracy, sensitivity, and specificity. GCS, SpO2, and respiratory rate were key predictors. These models may reduce unnecessary CT scans and optimize resource use. Further multicenter validation is needed to confirm their clinical utility.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.483 Zit.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
2020 · 7.588 Zit.
Calculation of average PSNR differences between RD-curves
2001 · 4.088 Zit.
Magnetic Resonance Classification of Lumbar Intervertebral Disc Degeneration
2001 · 3.880 Zit.
Vertebral fracture assessment using a semiquantitative technique
1993 · 3.591 Zit.
Autoren
- Amirmohammad Toloui
- Amir Ghaffari Jolfayi
- Hamed Zarei
- Arash Ansarian
- Amir Azimi
- Seyed Mohammad Forouzannia
- Rosita Khatamian Oskooi
- Gholamreza Faridaalaee
- Shayan Roshdi Dizaji
- Seyed Ali Forouzannia
- Seyedeh Niloufar Rafiei Alavi
- Mohammadreza Alizadeh
- Hadis Najafimehr
- Saeed Safari
- Alireza Baratloo
- Mostafa Hosseini
- Mahmoud Yousefifard