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Assessing the FAIRness of Deep Learning Models in Cardiovascular Disease Using Computed Tomography Images: Data and
Kirubel Biruk Shiferaw1, Atinkut Zeleke1, Dagmar Waltemath1
1Medical Informatics Laboratory, Institute for Community Medicine, University Medicine Greifswald, Germany.
Studies in Health Technology and Informatics
|May 19, 2023
Summary
Artificial intelligence (AI) shows promise in diagnosing cardiovascular diseases (CVD) from CT scans. However, challenges remain in making the associated data and code findable, accessible, interoperable, and reusable (FAIR).
Area of Science:
- Medical Imaging and Artificial Intelligence
- Cardiovascular Disease Diagnostics
- Data Science in Healthcare
Background:
- The application of artificial intelligence (AI) in medicine, particularly deep learning for cardiovascular disease (CVD) prediction using computed tomography (CT) images, has surged recently.
- Despite promising diagnostic results, significant challenges hinder the practical implementation and advancement of these AI models.
- Key obstacles include the findability, accessibility, interoperability, and reusability (FAIR) of the underlying data and source code.
Purpose of the Study:
- To identify recurring issues related to the FAIR principles in studies predicting cardiovascular diseases from CT images.
- To assess the current level of FAIRness for data and AI models used in this specific medical domain.
- To highlight the gap between AI's potential and its practical, reproducible application in clinical settings.
Main Methods:
- Systematic evaluation of published studies focusing on AI for CVD prediction from CT images.
- Utilized the Research Data Alliance (RDA) FAIR Data maturity model for assessment.
- Employed the FAIRshake toolkit to quantitatively evaluate the FAIRness of data and models.
Main Results:
- Identified persistent challenges across studies concerning the findability, accessibility, interoperability, and reusability of data and code.
- The assessment revealed a significant gap in adhering to FAIR principles, despite the advanced nature of AI in medicine.
- FAIRness remains a prominent barrier to the seamless integration and advancement of AI-driven diagnostic tools.
Conclusions:
- While AI offers groundbreaking potential for complex medical problems like cardiovascular disease diagnosis, its full impact is constrained by data and code accessibility issues.
- Addressing the findability, accessibility, interoperability, and reusability (FAIR) of data and models is crucial for realizing AI's promise in healthcare.
- Future research and development must prioritize robust FAIR data practices to ensure reproducible and scalable AI solutions in medicine.
Keywords:
Deep learningFAIR PrinciplesRDA FAIR Data maturity modelcardiovascular diseasecomputed tomographyMore Related Videos
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