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.
Przegląd badań
Status
Szczegółowy opis
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.
Typ studiów
Zapisy (Szacowany)
Kontakty i lokalizacje
Kontakt w sprawie studiów
- Nazwa: Jacopo Conteduca, MD
- Numer telefonu: 0039 3332280645
- E-mail: conteduca85@gmail.com
Lokalizacje studiów
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Lecce, Włochy, 73100
- Ospedale Vito Fazzi
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Kontakt:
- Jacopo Conteduca, MD
- Numer telefonu: 0039 3332280645
- E-mail: CONTEDUCA85@GMAIL.COM
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Kryteria uczestnictwa
Kryteria kwalifikacji
Wiek uprawniający do nauki
Akceptuje zdrowych ochotników
Płeć kwalifikująca się do nauki
Metoda próbkowania
Badana populacja
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.
Opis
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 studiów
Jak projektuje się badanie?
Szczegóły projektu
- Modele obserwacyjne: COHORT
- Perspektywy czasowe: PROSPECTIVE
Kohorty i interwencje
Grupa / Kohorta |
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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. |
Co mierzy badanie?
Podstawowe miary wyniku
Miara wyniku |
Opis środka |
Ramy czasowe |
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Agreement Between AI-Derived and Arthrometer-Derived Side-to-Side Difference in Anterior Tibial Translation
Ramy czasowe: 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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Miary wyników drugorzędnych
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
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Measurement Error of AI-Derived Anterior Tibial Translation Compared With Arthrometer Measurements
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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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Współpracownicy i badacze
Sponsor
Daty zapisu na studia
Główne daty studiów
Rozpoczęcie studiów (Szacowany)
Zakończenie podstawowe (Szacowany)
Ukończenie studiów (Szacowany)
Daty rejestracji na studia
Pierwszy przesłany
Pierwszy przesłany, który spełnia kryteria kontroli jakości
Pierwszy wysłany (Rzeczywisty)
Aktualizacje rekordów badań
Ostatnia wysłana aktualizacja (Rzeczywisty)
Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości
Ostatnia weryfikacja
Więcej informacji
Terminy związane z tym badaniem
Słowa kluczowe
Inne numery identyfikacyjne badania
- ASLLECCE-AILAX-2026-01
Plan dla danych uczestnika indywidualnego (IPD)
Planujesz udostępniać dane poszczególnych uczestników (IPD)?
Opis planu IPD
De-identified data may be made available from the corresponding author upon reasonable request, subject to institutional and ethical approval.
Informacje o lekach i urządzeniach, dokumenty badawcze
Bada produkt leczniczy regulowany przez amerykańską FDA
Bada produkt urządzenia regulowany przez amerykańską FDA
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