Detection of Osteophytes and Subchondral Cysts in the Knee with Use of Tomosynthesis
Bibliographic record
Abstract
PURPOSE: To evaluate the diagnostic performance of tomosynthesis in depicting osteophytes and subchondral cysts, with use of magnetic resonance (MR) imaging as the reference, and to test whether the lesions detected at radiography and tomosynthesis are associated with pain. MATERIALS AND METHODS: The study was approved by local institutional review board, and all subjects gave written informed consent. Forty subjects (80 knees) older than 40 years were recruited irrespective of knee pain or radiographic osteoarthritis. Knees were imaged with radiography, tomosynthesis, and MR imaging. Presence of osteophytes and subchondral cysts in four locations of tibiofemoral joint (medial and lateral femur and tibia) was recorded. Knee pain was assessed by using the Western Ontario and McMaster University pain subscale. RESULTS: MR imaging depicted 171 osteophytes and 51 subchondral cysts. Tomosynthesis had a higher sensitivity for osteophyte detection in left and right lateral femur (0.96 vs 0.75, P = .025, and 1.00 vs 0.71, P = .008, respectively), right medial femur (0.94 vs 0.72, P = .046), and right lateral tibia (1.00 vs 0.83, P = .046). For subchondral cyst detection, the sensitivity of tomosynthesis was 0.14-1.00 and that of radiography was 0.00-0.56. Both modalities had similar specificity for both lesions. Subjects with tomosynthesis-depicted osteophytes (odds ratio, 4.2-6.4; P = .001-.011) and medially located subchondral cysts (odds ratio, 6.7-17.8; P = .004-.03) were more likely to feel pain than those without. However, radiography-depicted osteophytes were more strongly associated with pain than were tomosynthesis-depicted osteophytes. CONCLUSION: Tomosynthesis depicted more osteophytes and subchondral cysts than did radiography. Subjects with tomosynthesis-depicted osteophytes and subchondral cysts were more likely to feel pain than those without such lesions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".