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NCT Number: NCT07833605

AI-Based Video Assessment of Anterior Knee Laxity

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.

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Key information

Conditions

ACL - Anterior Cruciate Ligament Deficiency

Age range

16 year–55 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

Each examiner will perform three consecutive Rolimeter measurements on both knees. Individual maneuvers will be video recorded so that the Rolimeter and AI measurements refer to the same mechanical examination. Videos will be pseudonymized and analyzed independently. The AI system will have no access to participant group allocation, MRI findings, injured side, Rolimeter measurements, or results from other videos belonging to the same participant.

A standardized metric reference positioned in the plane of the examined knee will be visible in each recording to permit estimation of displacement in millimeters. AI measurements will not be used for clinical decision-making.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

ACL-Deficient Group

  • Unilateral complete anterior cruciate ligament (ACL) injury confirmed by magnetic resonance imaging (MRI).
  • Clinical evidence of anterior knee instability compatible with ACL deficiency.
  • Contralateral knee without known ACL injury or previous ACL reconstruction.
  • ACL-Intact Control Group
  • No history of ACL injury or ACL reconstruction in either knee.
  • No clinical evidence of anterior knee instability.
  • Negative clinical assessment for ACL deficiency in both knees.

Exclusion criteria

  • 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.
  • Previous major knee surgery that could affect anterior tibial translation.
  • Moderate-to-severe knee osteoarthritis or radiographic osteoarthritis greater than Kellgren-Lawrence grade 1.
  • Acute fracture involving the examined lower limb.
  • Neurological, neuromuscular, or other conditions that could interfere with standardized knee examination.
  • Inability to tolerate or complete the standardized arthrometer examination.
  • Inability or unwillingness to provide informed consent or consent for study video recording.

Treatment and study plan

Primary outcomes

  1. Agreement Between AI-Derived and Arthrometer-Derived Side-to-Side Difference in Anterior Tibial Translation

    Time frame: Day 1 (index study examination)

    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.

Secondary outcomes

  1. Measurement Error of AI-Derived Anterior Tibial Translation Compared With Arthrometer Measurements

    Time frame: Day 1 (index study examination)

    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.

  2. Inter-Examiner Reliability of Arthrometer Measurements

    Time frame: Day 1 (index study examination)

    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.

  3. Agreement Between AI Measurements Derived From Examinations Performed by Different Examiners

    Time frame: Day 1 (index study examination)

    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.

  4. Diagnostic Accuracy of AI-Derived Side-to-Side Difference for ACL Injury

    Time frame: Day 1 (index study examination)

    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.

  5. diagnostic Accuracy of Arthrometer-Derived Side-to-Side Difference for ACL Injury

    Time frame: Day 1 (index study examination)

    The diagnostic performance of arthrometer-derived side-to-side difference for identifying complete anterior cruciate ligament injury will be evaluated using ROC curve analysis, including AUC, sensitivity, specificity, positive predictive value, and negative predictive value.

Study contacts

Contact information is provided by the study sponsor or research team.

Jacopo Conteduca, MD

CONTACT

[email protected]

0039 3332280645

Sponsors and collaborators

Lead sponsor

ASL Lecce

Other Gov

Registry information

Official study title

Artificial Intelligence-Based Video Assessment of Anterior Knee Laxity Compared With Rolimeter in Patients With Anterior Cruciate Ligament Injury: A Prospective Validation Study

Acronym: AI-LAX

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Sep 22, 2026
Registry last updated
Sep 22, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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