Diagnosis of intracranial hemorrhage

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Imaging Modalities for Intracranial Hemorrhage Diagnosis: CT, MRI, and Ultrasound

Computed Tomography (CT) is the primary imaging technique for the rapid diagnosis of intracranial hemorrhage due to its speed and ability to clearly show acute bleeding. CT scans are essential in emergency settings to identify the presence, location, and type of hemorrhage, guiding immediate treatment decisions 2478. Magnetic Resonance Imaging (MRI) is particularly valuable for assessing the age of a hemorrhage and is superior to CT in detecting subacute and chronic bleeds. MRI provides more detailed information about the evolution of blood products and is influenced by factors such as the age, location, and size of the hemorrhage, as well as technical and biological variables 247. Ultrasound is mainly used in pediatric cases, especially in neonates, but is less common in adults .

Types and Classification of Intracranial Hemorrhage

Intracranial hemorrhage is classified into five main subtypes based on the location of bleeding: epidural, subdural, subarachnoid, intraparenchymal, and intraventricular hemorrhages. Accurate identification of these subtypes is crucial for determining the underlying cause and appropriate management strategy 1910. Imaging findings on CT and MRI help differentiate these types, each of which has characteristic appearances at different stages of evolution 247.

Advances in Computer-Aided Diagnosis and Deep Learning

Recent research highlights the growing role of computer-aided diagnosis (CAD) systems and deep learning models in improving the speed and accuracy of intracranial hemorrhage detection. Deep learning approaches, such as convolutional neural networks (CNNs) and ensemble models, have demonstrated high accuracy in classifying and segmenting hemorrhages from CT images, with some models achieving over 97% accuracy 1356+3 MORE. These systems can assist radiologists by rapidly analyzing large datasets, identifying hemorrhage subtypes, and providing visual explanations for their decisions 1356+2 MORE. Optimized models, such as those using Bayesian optimization or hybrid feature extraction, further enhance diagnostic performance 110.

Clinical Implications and Future Directions

Timely and accurate diagnosis of intracranial hemorrhage is critical for patient survival and outcome. The integration of advanced imaging techniques and artificial intelligence-based tools can help address challenges such as radiologist shortages and the complexity of interpreting subtle or small bleeds 36810. New imaging applications, including magnetic resonance spectroscopy and diffusion tensor imaging, are being explored to provide deeper insights into tissue injury and recovery following hemorrhage .

Conclusion

The diagnosis of intracranial hemorrhage relies on rapid neuroimaging, primarily CT and MRI, to identify the presence, type, and stage of bleeding. Advances in deep learning and CAD systems are significantly improving diagnostic accuracy and efficiency, supporting clinicians in making timely, informed decisions for optimal patient care 1356+3 MORE.

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Most relevant research papers on this topic

Intracranial Brain Hemorrhage Diagnosis and Classification: A Hybrid Approach

Our hybrid machine-learning approach accurately diagnoses intracranial brain hemorrhage from CT scans, with 97.24% accuracy using the VGG16-MLP model and 97.02% using the VGG19-MLP model.

2023·

4citations

·M. Emon et al.

2023 6th International Conference on Information and Computer Technologies (ICICT)·

DOI

Intracranial hemorrhage: principles of CT and MRI interpretation

Intracranial hemorrhage imaging varies between CT and MRI, with CT focusing on density changes and MRI on signal intensity, technical factors, and biological factors.

Highly Cited

2001·

239citations

·P. Parizel et al.

European Radiology·

DOI

Retracted

[Retracted] Multimodal deep learning approach for identifying and categorizing intracranial hemorrhage

This ensemble-based deep learning approach accurately identifies and categorizes intracranial hemorrhage from CT scan images, with the soft voting ensemble achieving a test accuracy of 91.09% and the stacked generalization ensemble achieving 94.60%.

2023·

6citations

·A. S. et al.

Multimedia Tools and Applications·

DOI

Imaging of intracranial haemorrhage.

MRI is as sensitive as CT for detecting intracranial haemorrhage in hyperacute settings, and superior in subacute and chronic settings.

Highly Cited

2008·

211citations

·C. Kidwell et al.

The Lancet. Neurology·

DOI

Deep Learning Applied to Intracranial Hemorrhage Detection

Deep learning technology, EfficientDet, accurately detects intracranial hemorrhage in computed tomography scans with 92.7% accuracy and provides visual explanations using the Grad-CAM methodology.

2021·

45citations

·Luis Cortes-Ferre et al.

Journal of Imaging·

DOI

Detection of Intracranial Hemorrhage Using Deep Learning

Deep learning models can accelerate the identification of intracranial hemorrhages, achieving a 97% accuracy with a combination of EfficientNetB4 and Grad-Cam models.

2025·

0citations

·Sahayog Timalsina et al.

KEC Journal of Science and Engineering·

DOI

Intracranial hemorrhage: ultrasound, CT and MRI findings

Intracranial hemorrhage can be diagnosed using ultrasonography, CT, and MRI, with a thorough understanding of hematoma evolution and imaging techniques essential for accurate interpretation.

Highly Cited

2005·

97citations

·T. Huisman

European Radiology·

DOI

Intracranial hemorrhage detection

This paper presents a model that uses InceptionV3 and DenseNet models to detect intracranial hemorrhage, aiding in early detection and effective treatment of patients at high risk of severe bleeding in the brain.

2020·

19citations

·H. Menon et al.

Materials Today: Proceedings·

DOI

Intracranial Hemorrhage Detection in Head CT Using Double-Branch Convolutional Neural Network, Support Vector Machine, and Random Forest

This study presents a method for detecting intracranial hemorrhage types in head CT images using double-branch convolutional neural networks, support vector machines, and random forests, outperforming state-of-the-art methods in F1 score.

2020·

68citations

·Agata Sage et al.

Applied Sciences·

DOI

Intracranial Hemorrhage Classification From CT Scan Using Deep Learning and Bayesian Optimization

Our optimized DenseNet model accurately classifies intracranial hemorrhage from CT scans, aiding doctors in making better-informed decisions and providing better care for patients.

2023·

12citations

·Shifat E. Arman et al.

IEEE Access·

DOI

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