Evaluating Fairness of Mask R-CNN for Kidney Infection Detection based on Renal Scintigraphy

<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99m</sup>Tc-DMSA renal scan plays a crucial role in assessing functional abnormalities in the kidneys. A deep learning model, Mask R-CNN, showed much promise in diagnosing acute pyelonephritis, a type of kidney infection. This study evaluated the diagnostic performance and fairness of Mask R-CNN and Faster R-CNN using a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99m</sup>Tc-DMSA renal dataset. The classification results showed that Mask R-CNN achieved an accuracy of 0.89, while Faster R-CNN reached an accuracy of 0.88. Both models demonstrated strong classification capabilities for kidney conditions. Furthermore, the analysis of fairness across sex and age groups indicated that neither model exhibited significant bias, thereby supporting their suitability for clinical applications. Future research should consider integrating more patient data to further enhance the diagnostic capabilities and fairness assessments of the models.