Researchers at Johns Hopkins University are leveraging the power of machine learning to improve X-ray-guided pelvic fracture surgery, an operation to treat an injury commonly sustained during car crashes. SPR lays the foundation for automated surgical assistance and skill analysis systems that promise to maximize operating room efficiency. They simulated enough data to successfully train their own machine learning-powered SPR algorithm specifically for X-ray sequences. The researchers hope that Pelphix's success will motivate the routine collection and interpretation of X-ray data to enable further advances in surgical data science, ultimately improving the standard of care for patients. Surgical data science and surgical phase recognition—and approaches like Pelphix—are working to make that inscrutable 95% of surgery data observable, to patients' benefit."