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Abstract #2806

Preliminary Assessment of Racial Disparities in AI-based Prostate Cancer Detection on bpMRI

Patricia Johnson1,2, Tarun Dutt1, Madhur Nayan3, Angela Tong1, and Hersh Chandarana1,2
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Department of Urology, New York University Grossman School of Medicine, New York, NY, United States

Synopsis

Keywords: Prostate, Prostate

Motivation: AI models are being developed for prostate cancer (PCa) detection on MRI but potential racial bias in these models is not well understood.

Goal(s): This study evaluates potential racial bias in a deep learning (DL) classifier for clinically significant PCa detection on bi-parametric MRI (bpMRI)

Approach: An AI model using 3D ResNet50 encoders was trained on bpMRI data. Model performance was assessed, focusing on White and Black or African American patients.

Results: The model achieved an AUC of 0.83 for White patients and 0.77 for Black patients, indicating higher predictive accuracy for White patients.

Impact: This study reveals potential racial bias in an AI model for Prostate cancer detection on MRI, with lower predictive accuracy for Black patients. These findings emphasize the need for further work to ensure equitable AI algorithms for PCa detection.

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Keywords