TPF Machine Learning Algorithms
Study Details
Study Description
Brief Summary
To adopt a machine learning technique to decide whether operative or non-operative treatment will result in the best patient-outcome.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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 Design
Outcome Measures
Primary Outcome Measures
- Machine learning algorithm [1 year]
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.
Secondary Outcome Measures
- Clinical factors [1 year]
To identify clinical factors that help predicting the best treatment for future patients
- Radiological factors [1 year]
To identify radiological factors that help predicting the best treatment for future patients
Eligibility Criteria
Criteria
Inclusion Criteria:
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Age > 18 years
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Proximal tibia plateau fracture
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Patient is able to attend follow-up visits
Exclusion Criteria:
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Age < 18 years
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Bilateral fractures
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Neurologic disorders (ie paraplegia, CVA, dementia etc.)
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Not understanding Dutch or English
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | UZ Leuven | Leuven | Vlaams-Brabant | Belgium | 3000 |
Sponsors and Collaborators
- Universitaire Ziekenhuizen Leuven
Investigators
- Principal Investigator: Harm Hoekstra, Prof. MD, UZ Leuven
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- S64352