Abstract:
Bone fracture detection in radiograph is a fundamental task in clinical practice and a correct and timely evaluation of bone fractures has a very high correlation with patient outcome. If their plates, screws and implants consist of metal, then they are likely to be misread by the clinicians and by the automated devices, and therefore their diagnostic value will be reduced. This project had a motivator, to get over this limitation, and bring us towards more accurate fracture detection and interpretability. In order to reduce reliance on artifact-induced error, we developed a methodology which integrates modern deep learning with artifact reduction and prediction based on actual fracture appearance rather than artifact-induced signals. Comparisons with models presented in the literature showed that the system was generally more accurate, reliable and clinically usable than the other models. Alongside performance gains, the study also highlights the importance of interpretability - scientific descriptions that increase clinicians' confidence by ensuring safe application to the healthcare workflow. When broadly deployed, these findings can help limit misdiagnostic error and improve decision making, provide a path forward for the practical use of artificial intelligence (AI) systems in clinical medical imaging.
