Supporting AI-driven robotic orthopedic surgery planning with prospectively collected CT imaging and clinical data

Background

Robotic-assisted orthopedic surgery increasingly relies on preoperative imaging to optimize surgical planning and improve patient outcomes.

Complementing advances in robotic-assisted surgery, AI-driven approaches are being adopted to automate image interpretation, guide patient-specific implant selection, and enhance surgical precision¹.

Many real-world datasets lack standardized links between preoperative imaging, relevant clinical variables, and procedural data, limiting the development and validation of such AI-driven surgical planning algorithms.

Objective

A global medtech company engaged Aigora to obtain a regulatory-grade dataset integrating preoperative imaging, clinical data, and intraoperative information to support AI-driven innovation in robotic knee surgery planning.

Delivery

Aigora addressed this need by prospectively collecting and curating preoperative CT imaging, non-standard-of-care bone mineral density measurements, and implant information across multiple institutions and device manufacturers while adhering to customized imaging acquisition protocols.