Toward fast, objective assessment of orthopedic technical skill:: case-aware evaluation of deep learning for fluoroscopic image analysis

Surgeons are often trained through direct supervision in the operating room, but it can be difficult to measure technical skill objectively and give timely feedback. In some orthopedic procedures, skill can be assessed from images taken during surgery by measuring how well a wire is guided toward a target in the femur. The problem is that this analysis still requires extensive manual image annotation, making it too slow for routine use. This thesis takes a step toward faster, more objective assessment by developing computer methods to analyze surgery images automatically. It focuses on two early steps in a larger process: determining whether an image is a front-facing or side view, and outlining the femur in each image. These steps provide anatomical information needed for later analysis. A major finding is that how these models are evaluated matters just as much as how they are trained. Images from the same surgery are often very similar, so splitting them at random can make a model seem better than it really is. To avoid this, this thesis uses a case-aware evaluation strategy that keeps whole surgical cases separate. Under this framework, view classification performed very well on new cases, while femur segmentation showed mixed results: front-facing segmentation was promising, but side-view segmentation remained limited because the available dataset was too small and not representative enough. Overall, this thesis shows that automated image analysis could support faster, more objective orthopedic skill assessment, but only if performance is evaluated on truly new surgical cases.