Predicting fracture outcomes using AI (PRAISE)

Predicting fracture outcomes from clinical registry data using artificial intelligence-supplemented models for evidence-informed treatment (PRAISE)

Summary of the project:

The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI-derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture. Adult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study.

The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS.

Objectives: 

 1) Develop valid and reliable algorithms for identifying key distal radius fracture characteristics and treatment details using natural language processing and deep machine learning techniques applied to digital images, and surgical and radiology text reports.
2) Test whether AI-derived fracture and treatment characteristics improve the prediction of patient-reported outcome measures (PROMs) and clinical outcomes following distal radius fracture, compared to prediction models based on standard registry data.