Reducing Bias in Radiology with Topological Data Analysis

“By providing a more comprehensive, robust, and interpretable framework for analyzing medical imaging data, TDA has the potential to enhance the accuracy and fairness of radiological assessments.”

BUFFALO, NY - November 18, 2024 – A new editorial was published in Oncotarget's Volume 15 on November 12, 2024, entitled, “Mitigating bias in radiology: The promise of topological data analysis and simplicial complexes.”

In this publication, researchers Yashbir Singh, Colleen Farrelly, Quincy A. Hathaway, and Gunnar Carlsson from the Department of Radiology at Mayo Clinic in Rochester, MN, explore how a mathematical technique called Topological Data Analysis (TDA) can enhance the reliability and reduce bias in AI systems used for medical diagnosis. By addressing issues of fairness and accuracy in current AI tools, TDA holds the potential to transform the field of radiology.

Radiology increasingly relies on AI to analyze medical images like X-rays and Magnetic Resonance Imaging (MRIs). While these tools provide speed and efficiency, they can sometimes yield biased or inconsistent results due to limitations in the data or algorithms. Researchers suggest that TDA can address these challenges by capturing critical details in medical images—such as subtle tissue patterns or branching structures in blood vessels—that traditional methods might overlook.

TDA analyzes the "shape" and structure of data, which uncovers patterns and relationships beyond individual pixels. This innovative approach offers three key benefits: 1) It captures intricate features, such as looping blood vessels, 2) provides a more comprehensive analysis by examining interactions between pixel groups, creating a holistic view, and 3) enhances transparency that allows clinicians to better understand how AI reaches its conclusions and identify potential errors or biases.

AI tools in radiology are often trained on limited or unbalanced data, meaning they might not work as well for certain groups of people. This can lead to unfair or inaccurate diagnoses. TDA offers a way to fix that by creating more comprehensive and diverse data models. It can also handle noise and inconsistencies in images, like differences caused by different equipment or patient positions. 

“This mathematical framework has the potential to significantly improve the accuracy and fairness of radiological assessments, paving the way for more equitable patient care.”

In conclusion, this new approach has the potential to revolutionize how AI is used in radiology and improve diagnosis for everyone. While still in early development, researchers are optimistic about TDA’s ability to transform medical imaging. 

“As researchers and clinicians, we must continue to explore and develop these innovative approaches to ensure that the future of AI-assisted radiology is both highly accurate and equitable for all patients.”

Continue reading: DOI: https://doi.org/10.18632/oncotarget.28668

Correspondence to: Yashbir Singh - singh.yashbir@mayo.edu

Keywords: topological data analysis, simplicial complexes, radiology, medical imaging

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