A Validation Study of a Novel 3-Dimensional MRI Modeling Technique to Identify the Anatomic Insertions of the Anterior Cruciate Ligament
Bibliographic record
Abstract
BACKGROUND: Anatomic single bundle anterior cruciate ligament (ACL) reconstruction is the current gold standard in ACL reconstructive surgery. However, placement of femoral and tibial tunnels at the anatomic center of the ACL insertion sites can be difficult intraoperatively. We developed a "virtual arthroscopy" program that allows users to identify ACL insertions on preoperative knee magnetic resonance images (MRIs) and generates a 3-dimensional (3D) bone model that matches the arthroscopic view to help guide intraoperative tunnel placement. PURPOSE: To test the validity of the ACL insertion sites identified using our 3D modeling program and to determine the accuracy of arthroscopic ACL reconstruction guided by our "virtual arthroscopic" model. STUDY DESIGN: Descriptive laboratory study. METHODS: Sixteen cadaveric knees were prescanned using routine MRI sequences. A trained, blinded observer then identified the center of the ACL insertions using our program. Eight knees were dissected, and the centers of the ACL footprints were marked with a screw. In the remaining 8 knees, arthroscopic ACL tunnels were drilled into the center of the ACL footprints based on landmarks identified using our virtual arthroscopic model. Postprocedural MRI was performed on all 16 knees. The 3D distance between pre- and postoperative 3D centers of the ACL were calculated by 2 trained, blinded observers and a musculoskeletal radiologist. RESULTS: With 2 outliers removed, the postoperative femoral and tibial tunnel placements in the open specimens differed by 2.5 ± 0.9 mm and 2.9 ± 0.7 mm from preoperative centers identified on MRI. Postoperative femoral and tibial tunnel centers in the arthroscopic specimens differed by 3.2 ± 0.9 mm and 2.9 ± 0.7 mm, respectively. CONCLUSION: Our results show that MRI-based 3D localization of the ACL and our virtual arthroscopic modeling program is feasible and does not show a statistically significant difference to an open arthrotomy approach. However, additional refinements will be required to improve the accuracy and consistency of our model to make this an effective tool for surgeons performing anatomic single-bundle ACL reconstructions. CLINICAL RELEVANCE: Arthroscopic anatomic single-bundle ACL reconstruction is the current gold standard for ACL reconstruction; however, the center of the ACL footprint can be difficult to identify arthroscopically. Our novel modeling can improve the identification of this important landmark intraoperatively and decrease the risk of graft malposition and subsequent graft failure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".