ARTIFICIAL INTELLIGENCE(AI)-INTEGRATED COMPUTER-AIDED DIAGNOSIS(CAD) SCHEMES FOR DEVELOPING PROGNOSTIC MODELS IN STROKE STUDIES

The field of computer-aided diagnosis and detection (CAD) systems is rapidly expanding, with the integration of artificial intelligence (AI) significantly enhancing its capabilities. The development assists the doctors in detecting potential abnormalities, diagnosing the condition, and/or possible prognosis by examining the abnormal region. The CAD systemshold the potential to predict clinical events and future outcomes by employing biomarkers extracted from clinical data and/or imaging data. In this dissertation, the studies primarily focused on predicting the prognostics of multiple stroke conditions employing quantitative imaging biomarkers. The objective was to develop robust AI integrated CAD, AI-CAD, systems that would leverage advanced techniques to improve stroke outcome prediction accuracy and prognostic efficiency, specifically for Subarachnoid Hemorrhage (aSAH) stroke and Acute Ischemic Stroke (AIS) patients. This predictive capability would assist doctors in planning in-hospital treatment, allocating resources effectively, and guiding individualized follow-up care. Such an approach not only addresses critical needs requiring immediate attention but also offers financial benefits to many patients. Quantitative radiographic biomarkers predict prognosis in several neurological diseases. However, aSAH with its varied clinical course has limited objective tools to predict clinical outcomes accurately. The first study of the dissertation focused on developing a CAD scheme that explored the efficacy of a deep-learning classification architecture in predicting severalshort-term clinical events and functional outcomes, utilizing brain-computed tomography (CT) scans for aSAH patients. A retrospective dataset encompassing 60 aSAH patients was collated, each with two CT images acquired upon admission and after 10-14 days of admission (referred to as discharge in the study). The in-house developed CAD scheme was utilized for segmenting and labeling the brain regions and selecting CT slices based on the presence of blood clots in the relevant cisternal spaces. The transfer learning method was applied with two pre-trained architectures, DenseNet-121 and VGG16, selected as convolutional bases for feature extraction. The Convolution Based Attention Module (CBAM) was integrated atop the pre-trained architecture to enhance focus learning. Employing five-fold cross-validation, the developed prediction model assessed three clinical outcomes following aSAH: two shortterm clinical events typically occur within 72 hours of initial onset and one future functional outcome. The performance of the model was evaluated using multiple metrics. A comparison was conducted to analyze the impact of CBAM comparing the performance from the base models and CBAM integrated models. In the study, it was observed that the prediction model trained using CT images acquired at admission demonstrated higher accuracy in predicting short-term clinical outcomes. Conversely, the model was trained using CT images acquired on 10/14 days to accurately predict long-term clinical outcomes. Notably, for short-term outcomes, high sensitivity performances (0.88 and 0.86) were reported from the admission scan, while the sensitivity of (0.53 and 0.61) was reported from the discharge scan. This result showcased 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. To create a more clinically viable prediction model, it was necessary to incorporate domain knowledge alongside automatically extracted hierarchical deep features.. With that objective, the second study of this dissertation focused on analyzing the performance of an ensembled hybrid model with fused features that combine the strengths of radiomics anddeep learning techniques to objectively improve aSAH prognosis prediction. In the study, radiomic engineering and deep transfer learning techniques were applied to extract relevant features. Several predictive models, including radiomics, deep transfer learning, and combined ensemble machine learning approaches, were developed to predict four short-term and four long-term clinical outcomes. The analysis also included assessing admission and discharge scans to develop all prognostic models for eight events. Finally, receiver operating characteristic (ROC), accuracy, sensitivity, specificity, F1-score, and precision were computed to assess the model’s performance. The results indicated that hybrid ensemble models outperformed individual approaches, indicating complementary information that was extracted improved the prognostic performance. Additionally, the results of this study also demonstrated that admission scans predict short-term outcomes better, while discharge scans more effectively predict long-term outcomes, which matched the observation from my first study. This study highlighted the potential of quantitative hybrid ensemble CAD as a high-performing prognostic model for evaluating clinical complications in aSAH patients. To broaden the scope of this dissertation, my third study focused on more common types of stroke (AIS) patients. There is limited data on objective radiological parameters to predict functional outcomes in AIS patients. Although the conventional 10-point topographic system Alberta stroke program early CT score (ASPECTS) is widely used, it is limited by inter-ratervariability and the difficulty of detecting subtle ischemic changes on pre-intervention scans for clinical decision-making. Post-intervention scans can use these scoring systems to better define final cerebral infarct volume (CIV), an objective radiological marker of functional outcomes in AIS. Thus, this pilot study aimed to develop a composite radiological tool as an imaging biomarker by conducting a quantitative volumetric analysis of ASPECTS/Posterior Circulation (PC)-ASPECTS regions, CIV, and total brain volume (TBV). Initially, ASPECTS and PC-ASPECTS regions were annotated on anonymized scans and mapped to CIV. Various volumetric parameters were extracted, including TBV, CIV, and the ratio of CIV to TBV. These raw features enabled calculations of percentage volumes and facilitated summation and proportion analyses. Premorbid and admission clinical features were integrated into the models. Furthermore, raw and transformed features were used to train multiple classification algorithms. Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Lasso, and Elastic Net (EN) were applied to identify the most predictive features. Results showed that the Support Vector Machine (SVM) model, using EN-selected transformed features and clinical data, achieved the highest AUC (0.93±0.05). Alternatively, Logistic Regression (LR) and Extreme Gradient Boost (XGB) with RFE-selected transformed features and clinical data performed well (AUC 0.91±0.02). Models trained with transformed features generally outperformed others. Among the tested algorithms, the SVM model with EN-selected features was the top performer. This study highlights the potential of quantitative imaging biomarkers, combining volumetric features with clinical parameters, in developing a reliable prognostic model for AIS patients, surpassing traditional methods. This dissertation comprises three studies focused on my primary objective: developing prognostic models for stroke-affected patients. I concentrated on building various prediction models and optimizing the performance of deep architectures and hybrid ensemble models to predict multiple outcomes. Additionally, I explored diverse feature engineering and data optimization techniques to introduce innovative quantitative imaging markers utilizing machine learning, deep learning, and hybrid systems. The simulations and results from these studies demonstrate the proposed CAD schemes’ strong discriminative performance in stroke treatment, offering valuable support to radiologists in disease diagnosis and improving diagnostic accuracy.

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