Diffusion Tensor Imaging for Quantitative Assessment of Anterior Cruciate Ligament Injury Grades and Graft
Bibliographic record
Abstract
BACKGROUND: As the need for quantitative assessment of anterior cruciate ligament (ACL) injuries and ACL graft increases, diffusion tensor imaging (DTI) becomes a more valuable measuring tool. However, DTI changes in differing injury grades of ACL and longitudinal graft remain unclear. PURPOSE: To investigate the diagnostic performance of DTI in quantitatively assessing ACL injury severity and the development of ACL grafts within 6 months of surgery. STUDY TYPE: A cohort study. SUBJECTS: Thirty-five patients diagnosed with grades I-IV ACL injuries and 20 volunteers as controls were recruited. FIELD STRENGTH/SEQUENCE: -weighted, proton density (PD)-weighted, and DTI at 3.0T MRI. ASSESSMENT: ACL injury grades in arthroscopic images and DTI quantitative data were evaluated from July 2016 to July 2018. STATISTICAL TESTS: Chi-square test, analysis of variance, Spearman correlation analysis, and receiver operator characteristic (ROC) curves. RESULTS: Both fractional anisotropy (FA) (r = -0.898, P < 0.05) and apparent diffusion coefficient (ADC) (r = 0.851, P < 0.05) were significantly correlated with the severity of ACL injuries. The area under the curve (AUC) values for differentiation between low- and high-grade ACL injuries with FA and ADC were 0.973 and 0.963, respectively. Although there were no significant differences in FA (P > 0.05) and ADC (P > 0.05) between grades I and II ACL injuries or in ADC (P > 0.05) between grades III and IV, there were significant differences in FA and ADC between two grades (P < 0.05). There were significant differences in FA (P < 0.05) and ADC (P < 0.05) between normal ACL and 3-month graft postoperation, as well as in ADC values between 3-month and 6-month graft postoperation (P < 0.05). DATA CONCLUSION: DTI could be used to quantitatively evaluate the ACL injury grades and the development of ACL grafts. The diagnostic efficiency of FA values was higher than that of ADC values. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".