Artificial Intelligence to Detect Early Total Knee Replacement Implant Failure
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Aseptic Loosening of Prosthetic Joint. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Using Machine Learning to Detect and Predict Loosening NexGen Total Knee Replacement
Overview
The goal of this trial is to investigate whether Machine Learning (ML) can be used to detect small degrees of loosening, lucent zones, or any other changes on radiographs that might predict early failure following NexGen total knee replacement. Researchers will identify plain AP and lateral plain film radiographs from two groups of patients. Those who has NexGen total knee replacements (TKRs) that went on to failure, and those who has well performing TKRs. Radiographs from these two groups will be labelled as 'failure' and 'well performing' and will be processed through a machine learning algorithm. The algorithm will be successful if it is able to detect a NexGen TKR that went on to failure or went on to perform well. This will be determined by using a test set. The population will be adults who had the recalled a NexGen Total Knee Replacement with a standard tibial tray. It will include adults only, who has the TKR at University Hospitals Southampton between 2003 and 2022. Failure will be defined as revision of tibial or femoral components which is likely due to aspectic loosening. It will exclude washouts, exchange of poly, peri-prosthetic fractures, microbiologically confirmed infection. Well performing TKRs will be defined as patients who have had their TKR in situ for 10 years and have reported no significant symptoms.
Primary outcome measures
- Predictive accuracy of machine learning model [Time frame: Up to 21 years. Data starts from 2003.]
Eligibility criteria
Inclusion criteria
- Had a NexGen TKR between 2003 and 2022.
Exclusion criteria
- Below 18 yrs old.
- Revision surgery for any reason other than aseptic loosening
- patients who have not had a revision but who do not have a well functioning TKR.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-control
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT06724094 · T&O0238