A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence

Iskandar Tamimi; Joaquin Ballesteros; Almudena Perez Lara; Jimmy Tat; Motaz Alaqueel; Justin Schupbach; Yousef Marwan; Cristina Urdiales; Jesus Manuel Gomez-de-Gabriel; Mark Burman; Paul Andre Martineau

doi:10.1177/23259671211027543

A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence

Orthop J Sports Med. 2021 Sep 21;9(9):23259671211027543. doi: 10.1177/23259671211027543. eCollection 2021 Sep.

Affiliations

¹ Knee Division, Hospital Regional Universitario de Málaga, Málaga, Spain.
² ITIS Software, Universidad de Málaga, Málaga, Spain.
³ Department of Radiology, Hospital Regional Universitario de Málaga, Málaga, Spain.
⁴ Department of Surgery, Division of Orthopaedic Surgery, University of Toronto, Toronto, Ontario, Canada.
⁵ Division of Orthopaedic Surgery, McGill University Health Centre, Montreal, Canada.
⁶ Electronics Technology Department, Escuela de Ingeniería Telecomunicación, University of Malaga, Málaga, Spain.
⁷ Robotics and Mechatronics Group, Escuela de Ingenierías Industriales, University of Malaga, Málaga, Spain.

Abstract

Background: 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.

Hypothesis: 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.

Study design: Cross-sectional study; Level of evidence, 3.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: ACL; anterior cruciate ligament; anteroposterior lengths; artificial intelligence; bone slope; injury; machine learning; meniscal height; meniscal slope; prediction; risk.