AI-Based Video Assessment of Anterior Knee Laxity
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age16-55
SponsorASL Lecce
About this trial
This prospective observational validation study aims to evaluate the accuracy and reliability of artificial intelligence-based video analysis for quantifying anterior knee laxity. Participants with unilateral anterior cruciate ligament (ACL) injury and ACL-intact controls will undergo bilateral knee examination using a Rolimeter by two independent orthopedic surgeons. Three consecutive measurements will be obtained for each knee and examiner. Each examination maneuver will be video recorded using a standardized acquisition protocol including a metric reference of known dimensions.
The videos will subsequently be analyzed independently by a multimodal artificial intelligence system blinded to clinical diagnosis, MRI findings, injured side, examiner measurements, and contralateral knee measurements. AI-derived anterior tibial translation and side-to-side difference will be compared with Rolimeter measurements. The primary objective is to assess agreement between AI-derived and Rolimeter-derived side-to-side difference. Secondary objectives include inter-examiner reliability, AI repeatability and the diagnostic accuracy of AI-derived measurements for identifying ACL injury.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
General inclusion criteria
Age ≥16 years.
Ability to understand the study procedures and provide written informed consent.
Ability to undergo bilateral clinical assessment of anterior knee laxity.
Disqualifiers
Previous ACL reconstruction or other major ligament reconstruction of either knee.
Bilateral ACL injury.
Revision ACL injury or previous ACL surgery.
Concomitant complete injury of another major knee ligament requiring surgical treatment.
Trial population
Adults undergoing prospective bilateral assessment of anterior knee laxity at a single orthopedic center. The study population will include two cohorts: participants with a unilateral, complete anterior cruciate ligament (ACL) injury confirmed by magnetic resonance imaging and ACL-intact control participants without clinical evidence or history of ACL injury. All participants will undergo the same standardized bilateral knee examination using an arthrometer performed independently by two orthopedic examiners. Each examination maneuver will be video recorded for subsequent blinded AI-based assessment of anterior tibial translation.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
70 Participants
are grouped into 2 trial groupsGroup A: ACL-deficient participants
Group B: ACL-Intact Control Group
Trial outcomes
Primary outcomes
1
Agreement Between AI-Derived and Arthrometer-Derived Side-to-Side Difference in Anterior Tibial Translation
Agreement between the side-to-side difference (SSD, mm) in anterior tibial translation estimated by artificial intelligence from standardized knee examination videos and the corresponding SSD obtained with an arthrometer. Agreement will be assessed using intraclass correlation coefficient (ICC, absolute agreement) and Bland-Altman analysis, including mean bias and 95% limits of agreement.
Time frame
Day 1 (index study examination)Secondary outcomes
1
Measurement Error of AI-Derived Anterior Tibial Translation Compared With Arthrometer Measurements
The difference between AI-derived and arthrometer-derived measurements of anterior tibial translation will be quantified in millimeters using mean absolute error (MAE), root mean square error (RMSE), mean bias, and 95% limits of agreement.
Time frame
Day 1 (index study examination)2
Inter-Examiner Reliability of Arthrometer Measurements
Reliability of anterior tibial translation and side-to-side difference measurements obtained independently by two orthopedic examiners using an arthrometer. Inter-examiner reliability will be assessed using intraclass correlation coefficients with 95% confidence intervals and Bland-Altman analysis.
Time frame
Day 1 (index study examination)3
Agreement Between AI Measurements Derived From Examinations Performed by Different Examiners
Agreement between AI-derived anterior tibial translation and side-to-side difference obtained from videos of knee examinations performed independently by the two examiners. This outcome will assess the robustness of AI-derived measurements with respect to examiner-related variability.
Time frame
Day 1 (index study examination)4
Diagnostic Accuracy of AI-Derived Side-to-Side Difference for ACL Injury
The ability of AI-derived side-to-side difference to discriminate participants with complete anterior cruciate ligament injury from ACL-intact controls will be assessed using receiver operating characteristic (ROC) curve analysis. Area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value will be calculated.
Time frame
Day 1 (index study examination) Source ClinicalTrials.gov
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