Visualizing radiological data bias with persistence images

“Persistence images represent a powerful new tool in our ongoing efforts to visualize, understand, and mitigate biases in radiological data interpretation and AI model development.”

BUFFALO, NY - November 25, 2024 – A new editorial was published in Oncotarget's Volume 15 on November 12, 2024, entitled, “Visualizing radiological data bias through persistence images.”

This editorial highlights a powerful tool called "persistence images," which could improve how medical imaging and artificial intelligence (AI) systems are developed and used. Authors Yashbir Singh, Colleen Farrelly, Quincy A. Hathaway, and Gunnar Carlsson from the Department of Radiology, Mayo Clinic (Rochester, MN), provide a detailed explanation of how persistence images uncover hidden biases and advance fairness in healthcare AI.

AI is becoming a major part of healthcare, helping clinicians analyze X-rays, magnetic resonance imaging, and computed tomography scans. However, if the data used to train AI systems is biased, it could lead to unfair or inaccurate results. Derived from topological data analysis (TDA), persistence images transform complex medical scans into simple, stable visuals. These images make it easier to spot patterns or irregularities that could indicate bias. For example, they can reveal whether certain groups—such as patients of a specific age, gender, or ethnicity—are underrepresented in the data used to train AI systems.

“The use of persistence images in radiological analysis opens up new possibilities for identifying and addressing biases in both data interpretation and AI model training...” 

This could help ensure that AI systems work equitably for all patient groups, resulting in more reliable diagnoses and better outcomes.

In addition to detecting bias, persistence images also help filter out noise, or irrelevant details, from medical scans. This makes it easier for both AI systems and radiologists to focus on meaningful features in the images, improving overall accuracy. These insights help AI systems perform better and make more accurate, trustworthy decisions.

Despite their potential, persistence images face challenges. Generating persistence images for large datasets demands substantial computing power, while integration into clinical workflows requires user-friendly tools and specialized training for healthcare professionals.

As healthcare becomes more data-driven, tools like persistence images could transform how medical imaging is used. 

“By helping us visualize and address hidden biases, they can contribute to improved patient outcomes and more personalized healthcare delivery.”

In conclusion, this editorial envisions a future where advanced mathematical tools like persistence images play a vital role in eliminating bias and improving patient outcomes. Integrating these tools into clinical workflows could enhance radiological analysis, setting new standards for accuracy and equity in healthcare worldwide.

Continue reading: DOI: https://doi.org/10.18632/oncotarget.28670

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

Video short: https://www.youtube.com/watch?v=sQELv8oi3ew

Keywords: cancer, persistence images, data interpretation, radiology, topology

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