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Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery

NCT07333560 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 943

Last updated 2026-02-20

No results posted yet for this study

Summary

The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is:

Can a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery?

Patients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.

Conditions

  • Artificial Intelligence (AI)
  • Machine Learning
  • Joint Replacement
  • Predictive Model

Interventions

OTHER

Predictive Model for Early Mobility Recovery and Length of Stay

Application of a machine learning-based predictive algorithm to retrospectively and prospectively analyze clinical and perioperative data in patients undergoing hip or knee arthroplasty, without influencing clinical decision-making.

Sponsors & Collaborators

  • Istituto Ortopedico Rizzoli

    lead OTHER

Principal Investigators

  • Mattia Morri · IRCCS Istotuto Ortopedico Rizzoli

  • Morri · IRCCS Istotuto Ortopedico Rizzoli

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-02-28
Primary Completion
2027-12-31
Completion
2027-12-31

Countries

  • Italy

Study Locations

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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 NCT07333560 on ClinicalTrials.gov