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Updated: Jun 25, 2025
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
Published on: September 16, 2017
Deep learning-based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk
Meiyi Yang1,2, Lili Yang3, Qi Zhang3
1Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang, China.
Insights
A novel deep learning model, CCH-Network (CCHNet), accurately diagnoses chronic cerebral hypoperfusion (CCH) using MRI. This AI tool shows high sensitivity and specificity, improving diagnostic accuracy for CCH and aiding clinical decisions.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Neurology
Background:
- Chronic cerebral hypoperfusion (CCH) presents diagnostic challenges due to nonspecific symptoms.
- Accurate diagnosis of CCH is crucial for effective patient management.
Purpose of the Study:
- To enhance the diagnosis of CCH using Magnetic Resonance Imaging (MRI).
- To improve clinical decision-making and patient treatment outcomes through elevated diagnostic accuracy.
Main Methods:
- A retrospective study utilized 204 routine brain MRIs for training and testing, with 108 for validation.
- Developed CCH-Network (CCHNet), a deep learning model integrating convolution and Transformer modules for structural information capture.
- Employed an adversarial training method to enhance feature knowledge, generalization, and efficiency in CCH risk prediction.
Main Results:
- CCHNet achieved 91.6% AUC and 85.0% accuracy in the testing cohort.
- The model demonstrated 80.0% sensitivity and 90.0% specificity in the testing cohort.
- Validation cohort results showed 86.0% AUC, 84.2% accuracy, 83.3% sensitivity, and 84.7% specificity.
Conclusions:
- CCHNet significantly improved MRI diagnostic performance for CCH.
- The model offers high sensitivity and specificity, presenting a promising new method for CCH diagnosis.
Background:
Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that poses a diagnostic challenge due to its nonspecific symptoms.
Purpose:
To enhance the diagnosis of CCH and non-CCH through Magnetic Resonance Imaging (MRI), offering support in clinical decision-making and recommendations to ultimately elevate diagnostic accuracy and optimize patient treatment outcomes.
Methods:
In the retrospective research, we collected 204 routine brain magnetic resonance imaging (MRI) from March 1 to September 10 2022, as training and testing cohorts. And a validation cohort with 108 samples was collected from November 14 2022 to August 4 2023. MRI sequences were processed to obtain T1-weighted (T1WI) and T2-weighted (T2WI) sequence images for each patient. We propose CCH-Network (CCHNet), an end-to-end deep learning model, integrating convolution and Transformer modules to capture local and global structural information. Our novel adversarial training method improves feature knowledge capture, enhancing both generalization ability and efficiency in predicting CCH risk. We assessed the classification performance of the proposed model CCHNet by comparing it with existing state-of-the-art deep learning algorithms, including ResNet34, DenseNet121, VGG16, Convnext, ViT, Coat, and TransFG. To better validate model performance, we compared the results of the proposed model with eight neurologists to evaluate their consistency.
Results:
CCHNet achieved an AUC of 91.6% (95% CI: 86.8-99.1), with an accuracy (ACC) of 85.0% (95% CI: 75.6-95.2). It demonstrated a sensitivity (SE) of 80.0% (95% CI: 71.6-95.6) and a specificity (SP) of 90.0% (95% CI: 82.3-97.8) in the testing cohort. In the validation cohort, the model demonstrated an AUC of 86.0% (95% CI: 80.3-93.0), an ACC of 84.2% (95% CI: 70.2-93.6), a SE of 83.3% (95% CI: 68.3-95.5), and a SP of 84.7% (95% CI: 70.3-96.8).
Conclusions:
The model improved the diagnostic performance of MRI with high SE and SP, providing a promising method for the diagnosis of CCH.
