Acute ischemic stroke (AIS) remains a global health challenge, leading to long-term functional disabilities without timely intervention. Spatio-temporal (4D) Computed Tomography Perfusion (CTP) imaging is crucial for diagnosing and treating AIS due to its ability to rapidly assess the ischemic core and penumbra. Although traditionally used to assess acute tissue status in clinical settings, 4D CTP has also been explored in research for predicting stroke tissue outcomes. However, its potential for predicting functional outcomes, especially in combination with clinical metadata, remains unexplored. Thus, this work aims to develop and evaluate a novel multimodal deep learning model for predicting functional outcomes (specifically, 90-day modified Rankin Scale) in AIS patients by combining 4D CTP and clinical metadata. To achieve this, an intermediate fusion strategy with a cross-attention mechanism is introduced to enable a selective focus on the most relevant features and patterns from both modalities. Evaluated on a dataset comprising 70 AIS patients who underwent endovascular mechanical thrombectomy, the proposed model achieves an accuracy (ACC) of 0.77, outperforming conventional late fusion strategies (ACC = 0.73) and unimodal models based on either 4D CTP (ACC = 0.61) or clinical metadata (ACC = 0.71). The results demonstrate the superior capability of the proposed model to leverage complex inter-modal relationships, emphasizing the value of advanced multimodal fusion techniques for predicting functional stroke outcomes. • Integration of 4D CTP and clinical metadata to predict clinical stroke outcomes. • Novel multimodal fusion strategy using a cross-attention mechanism. • Using both data modalities leads to better results than single-modality models. • Proposed multimodal model outperforms conventional late fusion approaches.

OBJECTIVE: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. MATERIALS AND METHODS: Our framework utilizes synthetic neuroimages with known disease effects and sources of bias. We evaluated the impact of bias effects and the efficacy of 3 bias mitigation strategies in counterfactual data scenarios on a convolutional neural network (CNN) classifier. RESULTS: The analysis revealed that training a CNN model on the datasets containing bias effects resulted in expected subgroup performance disparities. Moreover, reweighing was the most successful bias mitigation strategy for this setup. Finally, we demonstrated that explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. DISCUSSION: The value of this framework is showcased in our findings on the impact of bias scenarios and efficacy of bias mitigation in a deep learning model pipeline. This systematic analysis can be easily expanded to conduct further controlled in silico trials in other investigations of bias in medical imaging AI. CONCLUSION: Our novel methodology for objectively studying bias in medical imaging AI can help support the development of clinical decision-support tools that are robust and responsible.