AI Fairness in Hip Bony Anatomy Segmentation: Analyzing and Mitigating Gender and Racial Bias in Plain Radiography Analysis

Abstract

Automatic segmentation of hip bony anatomy is a critical component of orthopedics enabling healthcare providers and clinicians to efficiently and objectively accomplish several medical image analysis tasks, including the diagnosis of hip fractures, arthritis, deformity, and dislocation. This autonomous process assists surgeons in preoperative planning by determining the location and size of surgical incisions, the placement of hip implants, and/or other surgical instruments. While deep learning computer vision algorithms for hip segmentation have demonstrated almost human-like performance in past literature, analyzing the fairness and any potential bias within such models has been very limited so far. Thus, the present work aims to provide a better understanding of any visible gender, ethnicity, and racial bias in hip bony anatomy segmentation using plain radiographs.

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