TPF Machine Learning Algorithms

Operative or Nonoperative Management of Tibial Plateau Fractures? Application of Machine Learning Algorithms to Assist in Treatment Decision

To adopt a machine learning technique to decide whether operative or non-operative treatment will result in the best patient-outcome.

Study Overview

• Status: Terminated
• Conditions: Tibial Plateau Fracture

Detailed Description

The overall goal is to adopt a machine learning technique to decide whether operative or non-operative treatment will result in the best patient-outcome.

The primary objectives are to identify the most suitable machine learning algorithm to predict the best treatment for future patients. Whether conservative or operative treatment will lead to the best patient outcome, will be decided on the predicted KOOS score. Several input factors, such as treatment (conservative or operative), number of fracture fragments, location of the fracture, soft tissue involvement,…for each patient will be used as training data for the algorithm. Some of these input data will be derived from CT-scans. Therefore, the CT scans will be segmented in Mimics, for which UZ Leuven recently purchased licenses. The output variable of the training data will be the KOOS score of each patient. Based on the input and output variable, the algorithm will determine a relation between these. For future patients of which the input variable are known, the output variable (=KOOS score) will be predicted both in case of operative and conservative treatment. We hypothesize that the prediction will be improved by adding more input data over time.

To secondary objective is to identify clinical and radiological factors that help predicting the best treatment for future patients.

As an outlook, the machine learning technique could be implemented in the future in clinical practice and utilized as a patient-specific planning tool for tibial plateau fracture management by aiding the surgeon to select the best treatment for a given case. The collected data in this registry will be used to validate the machine learning model. Patients will not yet be treated based on the results of the developed model, the trauma surgeon is responsible to decide which treatment option is best for the patient.

• Study Type: Observational
• Enrollment (Actual): 70

Contacts and Locations

Study Locations

• Belgium
  • Vlaams-Brabant
    • Leuven, Vlaams-Brabant, Belgium, 3000
      • UZ Leuven

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

Patients of UZ Leuven

Description

Inclusion Criteria:

• Age > 18 years
• Proximal tibia plateau fracture
• Patient is able to attend follow-up visits

Exclusion Criteria:

• Age < 18 years
• Bilateral fractures
• Neurologic disorders (ie paraplegia, CVA, dementia etc.)
• Not understanding Dutch or English

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Machine learning algorithm	To identify the most suitable machine learning algorithm that predicts the best treatment for future patients. The prediction will be improved over time by additional input.	1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Clinical factors	To identify clinical factors that help predicting the best treatment for future patients	1 year
Radiological factors	To identify radiological factors that help predicting the best treatment for future patients	1 year

Collaborators and Investigators

• Sponsor: Universitaire Ziekenhuizen KU Leuven
• Investigators: Principal Investigator: Harm Hoekstra, Prof. MD, UZ Leuven

Study record dates

Study Major Dates

• Study Start (Actual): October 5, 2020
• Primary Completion (Actual): June 27, 2023
• Study Completion (Actual): December 6, 2024

Study Registration Dates

• First Submitted: July 16, 2021
• First Submitted That Met QC Criteria: July 27, 2021
• First Posted (Actual): July 30, 2021

Study Record Updates

• Last Update Posted (Actual): July 29, 2025
• Last Update Submitted That Met QC Criteria: July 25, 2025
• Last Verified: July 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Knee Fractures; Wounds and Injuries; Leg Injuries; Fractures, Bone; Knee Injuries; Tibial Fractures; Tibial Plateau Fractures
• Other Study ID Numbers: S64352

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No