Maohua Lin

Abstract

Surgical planning and post-operative rehabilitation are hindered by fragmented workflows, limited personalization, and inefficient use of medical data. Current solutions rely heavily on manual interpretation of 2D CT and MRI images, standalone surgical planning software, generic implant templates, and disconnected rehabilitation tools. These approaches are time-intensive, prone to variability, and poorly suited for predicting patient-specific risks or outcomes. As a result, surgeons face longer planning cycles and uncertainty in implant selection, while patients often experience suboptimal outcomes, reduced engagement, and slower recovery.

Researchers at Florida Atlantic University have developed an AI-driven platform that integrates surgical planning, implant design, and post-operative rehabilitation into a unified, patient-specific workflow. The innovation automatically converts standard medical imaging into 3D anatomical models, applies machine learning to compare cases against historical outcomes, and supports personalized surgical and rehabilitation decisions. By unifying these capabilities in a single system, the platform improves precision, reduces manual effort, and enhances patient engagement compared to existing point solutions. The technology is currently in active development, with ongoing efforts focused on prototype refinement and validation.

FAU seeks to advance this innovation into the marketplace through licensing or development partnerships.

Benefit

Market Application