The research of Trustworthy AI for Magnetic Resonance Imaging (TAIMRI) Lab aims to improve the accuracy and reduce the costs of MRI-based diagnosis using AI methods, with a focus on neurological and musculoskeletal diseases. TAIMRI Lab is a collaboration between Erasmus MC, General Electric Healthcare, and the Erasmus University of Rotterdam.
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To improve the quality of MRI-based diagnosis with trustworthy AI methods. For this, we aim to optimize the full chain from image acquisition prescription, to image analysis and introduction of AI-supported acquisition and diagnosis in clinical practice. This can greatly improve the accuracy of diagnosis while reducing costs. At first, we focus on neurological and MSK diseases. Within the overall aim of TAIMRI Lab, there are five research projects. Specifically, we will:
develop smart, adaptive, MR imaging protocols for precision diagnosis;
develop trustworthy AI for integrated diagnostics of brain tumors;
develop trustworthy AI methods for improved diagnosis of bone and soft-tissue lesions on MRI;
develop an approach that ensures acceptance of AI technology in the daily clinical radiology setting. The smart adaptive protocols together with the deep-learning-based MR image reconstruction will form.
The smart adaptive protocols together with the deep-learning-based MR image reconstruction will form the basis for clinical MR scanning in the future. Key ingredients for these improvements come from the clinical expertise and experiences we have, and further develop, in the AI-assisted diagnostic approaches for brain tumors and bone/soft-tissue lesions. Specifically, in these approaches, image-based disease biomarkers will be created, which can be a subsequent target for the protocol adaptation. By focusing on two clinical domains, we enhance the generalizability of the developed methods and ensure broad applicability across disease domains.
Sustainable Development Goals
The Trustworthy AI For MRI Lab is part of the ROBUST program on Trustworthy AI-based Systems for Sustainable Growth which is financed under the NWO LTP funding scheme.
TAIMRI Lab is dedicated to advancing Sustainable Development Goal 3, Targets 4 and d, by leveraging artificial intelligence (AI) to improve the accuracy and efficiency of magnetic resonance imaging (MRI)-based diagnosis for neurological and musculoskeletal (MSK) conditions. These non-communicable diseases place a significant burden on healthcare systems and patients, and the incorporation of AI into diagnostic processes has the potential to not only improve patient outcomes and reduce costs but also accelerate treatment timelines.
Furthermore, the TAIMRI Lab also aligns with Sustainable Development Goal 10, Target 10.2, by striving to eliminate bias in data sets and promote social, economic, and political inclusion through the use of AI in healthcare. We recognize that low functional health literacy can result in passive decision-making and resistance to AI-assisted methods (Naik et al., 2011), and we take this into consideration in our research.
SDG 3: Ensure healthy lives and promote well-being for all at all ages
Target 3.4: By 2030, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being; Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction, and management of national and global health risks.
SDG 10: Reduce inequality within and among countries
Target 10.2: By 2030, empower and promote the social, economic, and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion, or economic or other status.
The lab consists of an interdisciplinary research group with expertise from imaging physics, image analysis, AI experts, clinical radiologists, and social scientists. This range of expertise in the development, validation, and socioeconomic valorization of medical imaging techniques allows for a multidisciplinary approach toward improving the quality of MRI-based diagnosis.