Artificial Intelligence (AI)FISIO_IANCT07333560
Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery
An observational study of Artificial Intelligence (AI) and Machine Learning, run by Istituto Ortopedico Rizzoli at 2 sites in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artificial Intelligence (AI), Machine Learning, Joint Replacement
- What is being tested?
- Predictive Model for Early Mobility Recovery and Length of Stay
- How many people
- 943 participants
- Where
- 2 sitesItaly
- Study dates
- 9 Mar 2026to Dec 2027
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: For the retrospective phase, data will be extracted from existing clinical and research databases at IRCCS Istituto Ortopedico Rizzoli. Pre-existing data collected between 2020 and 2023 will be used to develop automated predictive models. For the prospective phase, patients admitted to IRCCS Istituto Ortopedico Rizzoli for elective hip or knee arthroplasty between March 2026 and December 2027 will be consecutively enrolled. Newly collected data will be used to externally validate the predictive model, which will be applied without any modification (locked model).
Who may be able to join
- Adults aged 18 years or older
- Patients underwent elective hip or knee arthroplasty.
- Patients for whom postoperative physiotherapy was initiated.
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Italy2 sites, 2 recruiting
- Recruiting
SAITeR IRCCS Istituto Ortopedico Rizzoli
Bologna, 40100
Mattia Morri, +390516366694, mattia.morri@ior.it
- Not yet recruiting
Azienda U.S.L. - IRCCS di Reggio Emilia
Reggio Emilia
Alessia Pecorari, alessia.pecorari@ausl.re.it
About this study
Official title: Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.
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.
Read the detailed description
This observational study aims to develop and pre-validate a machine learning algorithm to predict early mobility recovery and hospital length of stay in patients undergoing elective hip or knee arthroplasty. The study includes a retrospective phase (2020-2023) using existing clinical and physiotherapy data, and a prospective phase (March 2026-December 2027) to validate the model in routine clinical practice.
Data Collection and Outcomes:
Mobility recovery: assessed by the ability to ascend and descend three steps within the first four postoperative days, recorded in the physiotherapy diary and electronic health record.
Length of stay: considered regular if discharged by the fifth postoperative day; longer stays are defined as prolonged.
Predictors: Baseline demographics (age, sex, BMI, ASA score, preoperative hemoglobin) and clinical/perioperative characteristics (type of surgery and anesthesia, initiation of physiotherapy, pain level, urinary catheter use, orthostatic intolerance).
Sample Size: 943 patients total (600 retrospective, 343 prospective), based on model development requirements and AUROC estimation.
Data Analysis: The dataset will be split into training, validation, and test sets. Multiple supervised learning algorithms (e.g., logistic regression, random forest, gradient boosting) will be compared. Model performance will be evaluated using AUROC, sensitivity, specificity, precision, F1-score, and calibration. Missing data will be handled with imputation or native algorithm methods when supported.
Model Validation: Prospective data will be used to assess model discrimination and calibration, and to identify potential temporal or clinical biases. Retraining may be performed using combined datasets to improve generalizability.
Study Flow: Retrospective patients identified via hospital records; prospective patients identified on the first postoperative physiotherapy session, provided with study information, and consented. Predictive results are stored in a separate registry inaccessible to treating clinicians.
Participating Centers:
IRCCS Istituto Ortopedico Rizzoli, Bologna - patient enrollment. Complex Structure of Medical Physics, Arcispedale S. Maria Nuova - data analysis and AI modeling.
- Being tested
- Predictive Model for Early Mobility Recovery and Length of Stay
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Predictive Model for Early Mobility Recovery and Length of StayOther
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve (AUROC) for discrimination ability of the machine learning predictive model
The discrimination ability of the machine learning predictive model will be assessed using the area under the receiver operating characteristic curve (AUROC). AUROC summarizes the trade-off between sensitivity and specificity across all possible classification thresholds. AUROC values range from 0.5 (no discrimination) to 1.0 (perfect discrimination). Higher values indicate better model performance. Values above 0.8 will be considered indicative of good discriminatory performance.
Measured: Through study completion, an average of 2 years
Secondary outcomes (2)
Calibration of the machine learning predictive model assessed by calibration plots
Agreement between predicted probabilities and observed outcomes will be evaluated using calibration plots. Calibration will be visually assessed by plotting predicted versus observed event probabilities.
Measured: through study completion, an average of 2 years
Predictive performance of the machine learning model assessed by precision and F1-score
Predictive performance will be evaluated using precision and F1-score derived from the confusion matrix by comparing predicted class labels with observed outcomes. Precision reflects the proportion of correctly predicted positive cases among all predicted positives. The F1-score represents the harmonic mean of precision and recall. Higher values indicate better predictive performance.
Measured: Through study completion, an average of 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jan 2026
- Study start9 Mar 2026
- Primary completionDec 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Artificial Intelligence (AI)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 943 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Istituto Ortopedico Rizzoli
- Sponsor type
- University, hospital or other
- Collaborators
- Azienda USL Reggio Emilia - IRCCS
- Responsible party
- Sponsor
- Investigators
- Mattia Morri, Principal investigator, IRCCS Istotuto Ortopedico RizzoliMorri, Principal investigator, IRCCS Istotuto Ortopedico Rizzoli
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07333560
- Sponsor's protocol ID
- 641/2025/Oss/IOR
- Acronym
- FISIO_IA
- Status last verified
- May 2026
- First submitted
- 16 Dec 2025
- Last updated
- 1 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The datasets generated and/or analyzed during the current study will be available from the Principal Investigator upon reasonable request.
Publications
Ribbons K, Cochrane J, Johnson S, Wills A, Ditton E, Dewar D, Broadhead M, Chan I, Dixon M, Dunkley C, Harbury R, Jovanovic A, Leong A, Summersell P, Todhunter C, Verheul R, Pollack M, Walker R, Nilsson M. Biopsychosocial based machine learning models predict patient improvement after total knee arthroplasty. Sci Rep. 2025 Feb 10;15(1):4926. doi: 10.1038/s41598-025-88560-w.
PubMed 39929870de Hond AAH, Steyerberg EW, van Calster B. Interpreting area under the receiver operating characteristic curve. Lancet Digit Health. 2022 Dec;4(12):e853-e855. doi: 10.1016/S2589-7500(22)00188-1. Epub 2022 Oct 18. No abstract available.
PubMed 36270955Hamel MB, Toth M, Legedza A, Rosen MP. Joint replacement surgery in elderly patients with severe osteoarthritis of the hip or knee: decision making, postoperative recovery, and clinical outcomes. Arch Intern Med. 2008 Jul 14;168(13):1430-40. doi: 10.1001/archinte.168.13.1430.
PubMed 18625924Gandhi R, Wasserstein D, Razak F, Davey JR, Mahomed NN. BMI independently predicts younger age at hip and knee replacement. Obesity (Silver Spring). 2010 Dec;18(12):2362-6. doi: 10.1038/oby.2010.72. Epub 2010 Apr 8.
PubMed 20379147Corbacioglu SK, Aksel G. Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value. Turk J Emerg Med. 2023 Oct 3;23(4):195-198. doi: 10.4103/tjem.tjem_182_23. eCollection 2023 Oct-Dec.
PubMed 38024184Baklola M, Reda Elmahdi R, Ali S, Elshenawy M, Mohamed Mossad A, Al-Bawah N, Mohamed Mansour R. Artificial intelligence in disease diagnostics: a comprehensive narrative review of current advances, applications, and future challenges in healthcare. Ann Med Surg (Lond). 2025 May 26;87(7):4237-4245. doi: 10.1097/MS9.0000000000003423. eCollection 2025 Jul.
PubMed 40851938