Company leadership underscored a integrated approach that combines imaging, machine learning, and surgical technology to help surgeons plan with confidence, execute precisely, and improve results.

Radiomics and ML methods are presented as enabling more informed, data-driven surgical planning before and during procedures to boost surgeon confidence and patient outcomes.

The narrative centers on a data-powered strategy that weaves together imaging, analytics, and surgical tools to enhance both planning and execution.

A featured podium presentation showcased a machine learning–based knee classification to support patient-specific alignment and intraoperative decisions, with early findings indicating improved consistency, reproducibility, and outcomes.

CT-based radiomics for shoulder arthroplasty were presented, examining how reconstruction settings affect radiomic measurements of muscle quality and identifying stable features that could serve as reliable biomarkers for planning.

Overall, Advita emphasizes that radiomics and ML-driven insights across joints support a data-powered approach to planning, execution, and improved patient outcomes.

The press release provides links to Advita Ortho’s website and social media profiles for additional information.

Advita Ortho is advancing AI-enabled orthopedic care, with research spanning knee, shoulder, and ankle arthroplasty highlighted at the ORS 2026 meeting as part of a broader push toward data-driven, intelligent surgical technologies.

The company announced 16 new research studies at ORS 2026, focusing on AI, machine learning, and advanced imaging analytics to enable personalized joint replacement.

Radiomics work differentiates patterns of glenoid deformity and, when combined with clinical data, improves preoperative outcome predictions for shoulder surgery.

Additional radiomics studies show that integrating radiomic patterns with clinical information enhances decision-making and informs preoperative planning for shoulder arthroplasty.

These radiomics findings collectively suggest better-informed preoperative decisions by identifying relevant deformity patterns and their likely impact on outcomes.