Development of a Deep Learning Model for Automating Implant Position in Total Hip Arthroplasty.

Background: Novel methods for annotating antero-posterior pelvis radiographs and fluoroscopic images with deep-learning models have recently been developed. However, their clinical use has been limited. Therefore, the purpose of this study was to develop a deep learning model that could annotate clinically relevant pelvic landmarks on both radiographic and fluoroscopic images and automate total hip arthroplasty (THA)-relevant measurements.

Novel dilation-erosion labeling technique allows for rapid, accurate and adjustable alignment measurements in primary TKA.

Background: Optimal implant position and alignment remains a controversial, yet critical topic in primary total knee arthroplasty (TKA). Future study of ideal implant position will require the ability to efficiently measure component positions at scale. Previous algorithms have limited accuracy, do not allow for human oversight and correction in deployment, and require extensive training time and dataset.

Deep learning-based fully automatic Risser stage assessment model using abdominal radiographs.

Background: Artificial intelligence has been increasingly used in medical imaging and has demonstrated expert level performance in image classification tasks.

Objective: To develop a fully automatic approach for determining the Risser stage using deep learning on abdominal radiographs.

Materials And Methods: In this multicenter study, 1,681 supine abdominal radiographs (age range, 9-18 years, 50% female) obtained between January 2019 and April 2022 were collected retrospectively from three medical institutions and graded manually using the United States Risser staging system.