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Artificial Intelligence to Detect Early Total Knee Replacement Implant Failure

NCT06724094 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 2105

Last updated 2025-05-13

No results posted yet for this study

Summary

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.

Conditions

  • Aseptic Loosening of Prosthetic Joint

Sponsors & Collaborators

  • University Hospital Southampton NHS Foundation Trust

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-08-31
Primary Completion
2025-12-31
Completion
2025-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06724094 on ClinicalTrials.gov