A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence
Bibliographic record
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: = .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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".