ARTIFICIAL INTELLIGENCE-BASED PREDICTION OF CLINICAL OUTCOMES IN PATIENTS SUSTAINING PROXIMAL HUMERAL FRACTURES

Our smartphones can recognize the pictures of our family, loved ones and friends. Face recognition software leverages artificial intelligence (AI), image recognition and other advanced technology to map, analyze and confirm the identity of a face.

We humans do a poor job when classifying the injury related to a patient sustaining a proximal humeral fracture. In consequence, there is great heterogeneity in the treatment of proximal humerus fractures. Moreover, offering relevant information to patients regarding the risk of complications or fracture sequelae is challenging, given that the current series are based on obsolete classifications, and the published series bring together just over hundreds of patients analyzed. With these limitations, patients have few opportunities to participate in decision-making about their injury.

The present project aim is to integrate new technologies for the prediction of relevant clinical results for the patients presenting a proximal humeral fracture. In brief, AI can help identify similar fracture patterns without human inference, while humans can feed the algorithm with variables of interest such as the functional outcomes and complications related to this particular type of fracture.