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Accurately predicting the clinical outcome of patients with aneurysmal subarachnoid hemorrhage (aSAH) presents notable challenges. This study sought to develop and assess a Computer-Aided Detection (CAD) scheme employing a deep-learning classification architecture, utilizing brain Computed Tomography (CT) images to forecast aSAH patients' prognosis. A retrospective dataset encompassing 60 aSAH patients was collated, each with two CT images acquired upon admission and after ten to 14 days of admission. The existing CAD scheme was utilized for preprocessing and data curation based on the presence of blood clot in the cisternal spaces. Two pre-trained architectures, DenseNet-121 and VGG16, were chosen as convolutional bases for feature extraction. The Convolution Based Attention Module (CBAM) was introduced atop the pre-trained architecture to enhance focus learning. Employing five-fold cross-validation, the developed prediction model assessed three clinical outcomes following aSAH, and its performance was evaluated using multiple metrics. A comparison was conducted to analyze the impact of CBAM. The prediction model trained using CT images acquired at admission demonstrated higher accuracy in predicting short-term clinical outcomes. Conversely, the model trained using CT images acquired on ten to 14 days accurately predicted long-term clinical outcomes. Notably, for short-term outcomes, high sensitivity performances (0.87 and 0.83) were reported from the first scan, while the sensitivity of (0.65 and 0.75) was reported from the last scan, showcasing the viability of predicting the prognosis of aSAH patients using novel deep learning-based quantitative image markers. The study demonstrated the potential of integrating deep-learning architecture with attention mechanisms to optimize predictive capabilities in identifying clinical complications among patients with aSAH.
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