RT Journal Article
T1 A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence.
A1 Tamimi, Iskandar
A1 Ballesteros, Joaquin
A1 Perez-Lara, Almudena
A1 Tat, Jimmy
A1 Alaqueel, Motaz
A1 Schupbach, Justin
A1 Marwan, Yousef
A1 Urdiales, Cristina
A1 Gomez-de-Gabriel, Jesus Manuel
A1 Burman, Mark
A1 Martineau, Paul Andre
K1 ACL
K1 Anterior cruciate ligament
K1 Anteroposterior lengths
K1 Artificial intelligence
K1 Bone slope
K1 Injury
K1 Machine learning
K1 Meniscal height
K1 Meniscal slope
K1 Prediction
K1 Risk
AB Supervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited. It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee. Cross-sectional study; Level of evidence, 3. Included were 50 adults who had undergone primary ACL reconstruction between 2008 and 2015. All patients were between 18 and 40 years of age at the time of surgery. Patients with a previous ACL injury, multiligament knee injury, previous ACL reconstruction, history of ACL revision surgery, complete meniscectomy, infection, missing data, and associated fracture were excluded. We also identified 50 sex-matched controls who had not sustained an ACL injury. For all participants, we used the preoperative magnetic resonance images to measure the anteroposterior lengths of the medial and lateral tibial plateaus as well as the lateral and medial bone slope (LBS and MBS), lateral and medial meniscal height (LMH and MMH), and lateral and medial meniscal slope (LMS and MMS). The AI predictor was created using Matlab R2019b. A Gaussian naïve Bayes model was selected to create the predictor. Patients in the ACL injury group had a significantly increased posterior LBS (7.0° ± 4.7° vs 3.9° ± 5.4°; P = .008) and LMS (-1.7° ± 4.8° vs -4.0° ± 4.2°; P = .002) and a lower MMH (5.5 ± 0.1 vs 6.1 ± 0.1 mm; P = .006) and LMH (6.9 ± 0.1 vs 7.6 ± 0.1 mm; P = .001). The AI model selected LBS and MBS as the best possible predictive combination, achieving 70% validation accuracy and 92% testing accuracy. A prediction model for primary ACL injury, created using machine learning techniques, achieved a >90% testing accuracy. Compared with patients who did not sustain an ACL injury, patients with torn ACLs had an increased posterior LBS and LMS and a lower MMH and LMH.
PB Sage Publications
SN 2325-9671
YR 2021
FD 2021-09-21
LK https://hdl.handle.net/10668/24557
UL https://hdl.handle.net/10668/24557
LA en
NO Tamimi I, Ballesteros J, Lara AP, Tat J, Alaqueel M, Schupbach J, et al. A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence. Orthop J Sports Med. 2021 Sep 21;9(9):23259671211027543
DS RISalud
RD Apr 8, 2025