Analysis and Quantification of Orbital Fractures: An Artificial Intelligence-Based Approach

Abstract

Trauma to the orbital region accounts approximately for 3% of all craniofacial injuries, with a mean hospitalization cost of US$ 35,500 per case. Management of orbital fractures is challenging as a substantial amount of individual judgment is required to determine the need for surgery. The definitive indications for surgery include muscle entrapment, visible enophthalmos (sunken eye), diplopia (double vision) or hypo-globus (vertical asymmetry of the globes). Otherwise, the need for surgery is based on subjective factors such as the relative size of the fracture, which is used as a proxy for the risk of developing enophthalmos once healing has progressed. Other factors considered include calculating the change in orbital volume, presence, and extent of tissue herniation, etc. However, these methodologies lack robust sample sizes or controls for population-based variation. In addition, sophisticated systems to standardize orbital fracture surgical intervention are impractical for real-time clinical application and have low reproducibility and not adequately validated against clinical outcomes. In essence, complications arising from untreated orbital fractures pose a unique predictive analytics problem, and the need for an effective and informed risk assessment tool to help determine the need for surgical intervention is highly desired.

Analytics utilizing Artificial Intelligence (AI) provides a unique opportunity to address this issue. An innovative AI-based system was be developed to and classify orbital fractures at risk of future complications without surgery. Ultimately the analytical tools developed through this project will be used to develop predictive tools to link fracture characteristics with the likelihood of developing poor clinical outcomes like enophthalmos, hypoglobus and late-stage diplopia. This novel AI approach seeks to resemble the decision-making process of a cranio-maxillofacial surgeon by automatically classifying demographic, clinical, radiographic and morphological data from patients and their injury characteristics. This study will provide a proof-of-concept framework in AI-based computational modeling for assessment of outcomes for craniofacial trauma.