MedTrace Logo

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

NCT06634654 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 197

Last updated 2026-03-04

No results posted yet for this study

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.

Conditions

Interventions

PROCEDURE

Total Knee Arthroplasty

Total Knee Arthroplasty is performed using conventional surgical techniques.

DIAGNOSTIC_TEST

Multifaceted diagnostic assessments

Multifaceted diagnostic assessments involving genetic analysis, biomechanical data collection, radiographic imaging, and psychological evaluations.

BEHAVIORAL

Follow-ups

Postoperative follow-up includes behavioral interventions, such as lifestyle counseling and rehabilitation programs, tailored based on AI-driven insights into individual patient recovery profiles.

GENETIC

Genetic screening

Genetic screening and analysis, including whole exome sequencing, are conducted to identify genetic markers that might influence the outcomes of knee arthroplasty. This data is utilized within AI models to predict patient-specific surgical outcomes and recovery processes.

Sponsors & Collaborators

  • Fondazione Policlinico Universitario Campus Bio-Medico

    lead OTHER

Study Design

Allocation
NA
Purpose
TREATMENT
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-10-14
Primary Completion
2026-06-30
Completion
2029-12-31

Countries

  • Italy

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06634654 on ClinicalTrials.gov