ACL - Anterior Cruciate Ligament DeficiencyAI-LAXNCT07833605
AI-Based Video Assessment of Anterior Knee Laxity
An observational study of ACL - Anterior Cruciate Ligament Deficiency, run by ASL Lecce at 1 site in Italy.
Trial summary
This prospective observational validation study aims to evaluate the accuracy and reliability of artificial intelligence-based video analysis for quantifying anterior knee laxity. Read more about this study
- Who is it for?
- Ages 16 to 55 yearsHealthy volunteers can take part
- Conditions the study is for
- ACL - Anterior Cruciate Ligament Deficiency
- How many people
- 70 participants
- Where
- 1 siteItaly
- Study dates
- 15 Sep 2026to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 to 55 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Study 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.
Who may be able to join
- 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).
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- 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.
Who cannot join
- 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.
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- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Italy1 site
Ospedale Vito Fazzi
Lecce, 73100
Jacopo Conteduca, MD, 0039 3332280645, CONTEDUCA85@GMAIL.COM
About this study
Official title: Artificial Intelligence-Based Video Assessment of Anterior Knee Laxity Compared With Rolimeter in Patients With Anterior Cruciate Ligament Injury: A Prospective Validation Study
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.
Read the detailed description
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.
- Keywords
- ACL, AI, ARTHROMETER
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| ACL-deficient participants | Adults with a unilateral, complete anterior cruciate ligament (ACL) injury confirmed by magnetic resonance imaging (MRI). Participants will undergo bilateral anterior knee laxity assessment using a Rolimeter by two independent orthopedic examiners. Three consecutive measurements will be obtained for each knee and examiner, and each examination maneuver will be video recorded for subsequent blinded AI-based analysis. The contralateral knee will serve as the within-subject reference for calculation of the side-to-side difference. | |
| ACL-Intact Control Group | Adults without a history or clinical evidence of ACL injury or knee instability. Participants will undergo the same standardized bilateral anterior knee laxity assessment performed by two independent orthopedic examiners using a Rolimeter, with three consecutive measurements for each knee and examiner. Each examination maneuver will be video recorded and subsequently analyzed by the AI system using the same protocol applied to the ACL-deficient cohort. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Day 1 (index study examination)
Secondary outcomes (5)
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.
Measured: Day 1 (index study examination)
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.
Measured: Day 1 (index study examination)
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.
Measured: Day 1 (index study examination)
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.
Measured: Day 1 (index study examination)
diagnostic Accuracy of Arthrometer-Derived Side-to-Side Difference for ACL Injury
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.
Measured: Day 1 (index study examination)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Sep 2026
- Study start15 Sep 2026 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ACL - Anterior Cruciate Ligament DeficiencyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 70 participants (estimated)
Sponsor and investigators
- Lead sponsor
- ASL Lecce
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Jacopo Conteduca, ASL Lecce
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07833605
- Sponsor's protocol ID
- ASLLECCE-AILAX-2026-01
- Acronym
- AI-LAX
- Status last verified
- Sep 2026
- First submitted
- 12 Sep 2026
- Last updated
- 22 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: De-identified data may be made available from the corresponding author upon reasonable request, subject to institutional and ethical approval.
Study at a glance
From the registry record, last updated by the sponsor on 22 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Based Video Assessment of Anterior Knee Laxity Compared With Rolimeter in Patients With Anterior Cruciate Ligament Injury: A Prospective Validation Study
- ClinicalTrials.gov ID
- NCT07833605
- Acronym / sponsor ID
- AI-LAX / ASLLECCE-AILAX-2026-01
- Keywords
- ACL, AI, ARTHROMETER
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 70 participants (estimated)
- Lead sponsor
- ASL LecceOther government
- Study start
- 15 Sep 2026 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 22 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| ACL-deficient participants | – | –Adults with a unilateral, complete anterior cruciate ligament (ACL) injury confirmed by magnetic resonance imaging (MRI). Participants will undergo bilateral anterior knee laxity assessment using a Rolimeter by two independent orthopedic examiners. Three consecutive measurements will be obtained for each knee and examiner, and each examination maneuver will be video recorded for subsequent blinded AI-based analysis. The contralateral knee will serve as the within-subject reference for calculation of the side-to-side difference. |
| ACL-Intact Control Group | – | –Adults without a history or clinical evidence of ACL injury or knee instability. Participants will undergo the same standardized bilateral anterior knee laxity assessment performed by two independent orthopedic examiners using a Rolimeter, with three consecutive measurements for each knee and examiner. Each examination maneuver will be video recorded and subsequently analyzed by the AI system using the same protocol applied to the ACL-deficient cohort. |
Who may qualify
- Ages
- 16 to 55 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study 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.
Key inclusion criteria (12 listed)
- 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
Key exclusion criteria (10 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
5 secondary outcomes
Measurement Error of AI-Derived Anterior Tibial Translation Compared With Arthrometer Measurements
Time frame: Day 1 (index study examination)
Inter-Examiner Reliability of Arthrometer Measurements
Time frame: Day 1 (index study examination)
Agreement Between AI Measurements Derived From Examinations Performed by Different Examiners
Time frame: Day 1 (index study examination)
Diagnostic Accuracy of AI-Derived Side-to-Side Difference for ACL Injury
Time frame: Day 1 (index study examination)
diagnostic Accuracy of Arthrometer-Derived Side-to-Side Difference for ACL Injury
Time frame: Day 1 (index study examination)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Sep 2026.
- Individual participant dataSharing planned: No. De-identified data may be made available from the corresponding author upon reasonable request, subject to institutional and ethical approval.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of ACL - Anterior Cruciate Ligament Deficiency, run by ASL Lecce at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 12 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jacopo Conteduca, MD (0039 3332280645, conteduca85@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement Between AI-Derived and Arthrometer-Derived Side-to-Side Difference in Anterior Tibial Translation (time frame: Day 1 (index study examination)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Sep 2026.