Goal: The goal of this interventional study is to understand how multimodal preoperative data can predict outcomes after Total Knee Arthroplasty (TKA) and improve personalized medicine practices. Participant Population: The study will enroll 197 patients suffering from symptomatic, end-stage knee osteoarthritis, who are above 18 years old and have functionally intact ligaments. Main Questions: * Can multimodal preoperative data, genetic predisposition, and psycho-behavioral characteristics predict outcomes after TKA? * Can AI models effectively use this data to customize prostheses and surgical interventions, and predict patient outcomes? Comparison Group Information (If applicable): Not specified in the provided details. Participant Tasks: * Undergo TKA as per the normal clinical routine. * Participate in pre- and post-surgical follow-ups including: * Clinical-functional assessments. * Administration of clinical scores. * Collection of biological samples. * Biomechanical analysis using a stereophotogrammetric system. * Provide data for the comprehensive multimodal indexed database.
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Change From Baseline in Knee Society Score (KSS) at 12 months
Timeframe: Before surgery (Baseline) and at 12 months postoperatively
Change From Baseline in Oxford Knee Score (OKS) at 12 months
Timeframe: Before surgery (Baseline) and at 12 months postoperatively
Change From Baseline in Knee Injury and Osteoarthritis Outcome Score (KOOS) at 12 months
Timeframe: Before surgery (Baseline) and at 12 months postoperatively
Change From Baseline in Forgotten Joint Score Short Form (FJS-12) at 12 months
Timeframe: Before surgery (Baseline) and at 12 months postoperatively
Umile Giuseppe Longo, MD, MSc, PhD