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.

Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets. Therefore, all layers of the PD classifier were frozen, except for the last layer of the network, which was replaced by a linear layer that was exclusively re-trained to predict three potential bias types (biological sex, scanner type, and originating site). Our findings based on a large database consisting of 1880 MRI scans collected across 41 centers show that the feature space of the established PD model (74% accuracy) can be used to classify sex (75% accuracy), scanner type (79% accuracy), and site location (71% accuracy) with high accuracies despite this information never being explicitly provided to the PD model during original training. Overall, the results of this study suggest that trained image-based classifiers may use unwanted shortcuts that are not meaningful for the actual clinical task at hand. This finding may explain why many image-based deep learning models do not perform well when applied to data from centers not contributing to the training set.