Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery
Recruiting now
Conditions studied: Artificial Intelligence (AI), Machine Learning, Joint Replacement, Predictive Model
In brief
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.
Key facts
- Study ID
- NCT07333560
- Run by
- Istituto Ortopedico Rizzoli
- People needed
- 943
- Starts
- 2026-03-09
- Expected to finish
- 2027-12-01
- Last updated by the study team
- 2026-06-01
Who can join
Age: 18 and older. Sex: any. Healthy volunteers: not accepted.
You may qualify if…
- Adults aged 18 years or older
- Patients underwent elective hip or knee arthroplasty.
- Patients for whom postoperative physiotherapy was initiated.
You may not qualify if…
- Patients who underwent surgery for oncologic disease, femoral fracture, or revision joint arthroplasty.
- Patients for whom postoperative physiotherapy was not provided due to postoperative complications
- clinical data are unavailable.
Where it is running
- SAITeR IRCCS Istituto Ortopedico Rizzoli — Bologna, Italy (enrolling)
- Azienda U.S.L. - IRCCS di Reggio Emilia — Reggio Emilia, Italy
Full record on ClinicalTrials.gov
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