Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries
Bibliographic record
Abstract
BACKGROUND: Technological innovation is a key component of orthopaedic surgery. With the integration of powerful technologies in surgery and clinical practice, artificial intelligence (AI) may become an important tool for orthopaedic surgeons in the future. Through adaptive learning and problem solving that serve to constantly increase accuracy, machine learning algorithms show great promise in orthopaedics. PURPOSE: To investigate the current and potential uses of AI in the management of anterior cruciate ligament (ACL) injury. STUDY DESIGN: Systematic review; Level of evidence, 3. METHODS: A systematic review of the PubMed, MEDLINE, Embase, Web of Science, and SPORTDiscus databases between their start and August 12, 2020, was performed by 2 independent reviewers. Inclusion criteria included application of AI anywhere along the spectrum of predicting, diagnosing, and managing ACL injuries. Exclusion criteria included non-English publications, conference abstracts, review articles, and meta-analyses. Statistical analysis could not be performed because of data heterogeneity; therefore, a descriptive analysis was undertaken. RESULTS: A total of 19 publications were included after screening. Applications were divided based on the different stages of the clinical course in ACL injury: prediction (n = 2), diagnosis (n = 12), intraoperative application (n = 1), and postoperative care and rehabilitation (n = 4). AI-based technologies were used in a wide variety of applications, including image interpretation, automated chart review, assistance in the physical examination via optical tracking using infrared cameras or electromagnetic sensors, generation of predictive models, and optimization of postoperative care and rehabilitation. CONCLUSION: There is an increasing interest in AI among orthopaedic surgeons, as reflected by the applications for ACL injury presented in this review. Although some studies showed similar or better outcomes using AI compared with traditional techniques, many challenges need to be addressed before this technology is ready for widespread use.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".