Automatic segmentation of knee bony anatomy is essential in orthopedics, and it has been around for several years in both pre-operative and post-operative settings. While deep learning algorithms have demonstrated exceptional performance in medical image analysis, the assessment of fairness and potential biases within these models remains limited. This study aims to revisit deep learning-powered knee-bony anatomy segmentation using plain radiographs to uncover visible gender and racial biases. The current contribution offers the potential to advance our understanding of biases, and it provides practical insights for researchers and practitioners in medical imaging. The proposed mitigation strategies mitigate gender and racial biases, ensuring fair and unbiased segmentation results. Furthermore, this work promotes equal access to accurate diagnoses and treatment outcomes for diverse patient populations, fostering equitable and inclusive healthcare provision.

Machine learning using deep convolutional neural networks (CNNs) can be used to detect fractures in plain radiographs, according to a new study published in Clinical Radiology. A team of researchers from the U.K. taught the CNNs using lateral wrist radiographs performed at a single facility from January 2015 to January 2016. Each image was classified as "fracture" or "no fracture" based on the existing radiology report. The distinction was personally verified by a human specialist before data was used to "train" the CNN. Overall, the area under the receiver operator characteristic curve (AUC) was 0.954, a number the authors said provided a proof of concept.