An Artificial Intelligence-based Approach in Total Knee Arthroplasty: From Inflammatory Responses to Personalized Medicine

Summary

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.

Eligibility

Inclusion Criteria:

1. Symptomatic, end-stage knee osteoarthritis
2. Ligaments functionally intact
3. Age: older than18 years old

Exclusion Criteria:

1. Neurological or other conditions affecting patients ability to join walking trials
2. Inflammatory or infectious arthritis
3. Previous articular fracture or knee surgery (excluding knee arthroscopy and meniscal surgery)
4. Active tumors or pregnancy.

Conditions2

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