The Impact of AI on Fracture Detection in MSK X-rays and Patient Outcomes

About this event

Fractures are a common type of injury that can have serious consequences if not detected and treated promptly. X-rays have been used widely as a diagnostic tool for detecting fractures, but the accuracy of fracture detection can vary depending on various factors such as the complexity of the fracture, experience of the radiologist and the quality of the X-ray image. In this webinar, we will understand these challenges in a bit more depth and discuss how advancements in fracture detection technology can improve patient outcomes in musculoskeletal (MSK) X-rays.
Missed or delayed diagnosis are unfortunately a frequent occurrence worldwide and Qure.ai has developed a robust AI solution to detect and localise fractures in X-Rays of 15 anatomies for adult (18+) patients. With <30s of processing time, it has the potential to bring radical changes to delivering timely patient care and better outcomes.

Topics of discussion will include: (not exhaustive)

§ What is it like for a radiologist to read and report X-Rays for fractures in a pool of radiographs?
§ What are some common challenges that lead to missed/incorrect diagnosis (perception bias, satisfaction of search, workload, lack of expertise etc.)
§ Discussion on Complex fractures, how different modalities are used for confirming certain type of fractures
§ Impact of missing these fractures for patients (can also segregate the patient demographic to understand the impact better)
§ Different settings in which Fractures are read and how the challenges differ for each setting
§ Can there be a one size fits all solution to the problem? What kind of bottlenecks exist beyond fracture detection? (time to imaging, lack of equipments, lack of infrastructure or resources for immediate treatment)