AI-Based Video Assessment of Anterior Knee Laxity (AI-LAX)
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.
Aperçu de l'étude
Statut
Les conditions
Description détaillée
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.
Type d'étude
Inscription (Estimé)
Contacts et emplacements
Coordonnées de l'étude
- Nom: Jacopo Conteduca, MD
- Numéro de téléphone: 0039 3332280645
- E-mail: conteduca85@gmail.com
Lieux d'étude
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Lecce, Italie, 73100
- Ospedale Vito Fazzi
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Contact:
- Jacopo Conteduca, MD
- Numéro de téléphone: 0039 3332280645
- E-mail: CONTEDUCA85@GMAIL.COM
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Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
Accepte les volontaires sains
Sexes éligibles pour l'étude
Méthode d'échantillonnage
Population étudiée
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.
La description
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.
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
- Modèles d'observation: COHORT
- Perspectives temporelles: PROSPECTIVE
Cohortes et interventions
Groupe / Cohorte |
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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. |
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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. |
Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
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Agreement Between AI-Derived and Arthrometer-Derived Side-to-Side Difference in Anterior Tibial Translation
Délai: Day 1 (index study examination)
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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. |
Day 1 (index study examination)
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Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Measurement Error of AI-Derived Anterior Tibial Translation Compared With Arthrometer Measurements
Délai: Day 1 (index study examination)
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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. |
Day 1 (index study examination)
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Inter-Examiner Reliability of Arthrometer Measurements
Délai: Day 1 (index study examination)
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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. |
Day 1 (index study examination)
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Agreement Between AI Measurements Derived From Examinations Performed by Different Examiners
Délai: Day 1 (index study examination)
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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. |
Day 1 (index study examination)
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Diagnostic Accuracy of AI-Derived Side-to-Side Difference for ACL Injury
Délai: Day 1 (index study examination)
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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. |
Day 1 (index study examination)
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diagnostic Accuracy of Arthrometer-Derived Side-to-Side Difference for ACL Injury
Délai: Day 1 (index study examination)
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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. |
Day 1 (index study examination)
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Collaborateurs et enquêteurs
Parrainer
Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Estimé)
Achèvement primaire (Estimé)
Achèvement de l'étude (Estimé)
Dates d'inscription aux études
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Plus d'information
Termes liés à cette étude
Mots clés
Autres numéros d'identification d'étude
- ASLLECCE-AILAX-2026-01
Plan pour les données individuelles des participants (IPD)
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Description du régime IPD
De-identified data may be made available from the corresponding author upon reasonable request, subject to institutional and ethical approval.
Informations sur les médicaments et les dispositifs, documents d'étude
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