Comparison of 3D Ultrasound Imaging to Computed Tomography in Knee Osteophyte Depiction
Bibliographic record
Abstract
Background Osteophytes (marginal bony outgrowths) are a common radiographic marker of osteoarthritis (OA) and joint degeneration. However, due to their variable morphologic composition, osteophytes are not accurately depicted using conventional imaging modalities. This represents problems for evaluating the anatomical changes of the osteoarthritic joint, and for the design of surgical interventions that rely on the accuracy of pre‐operative images. Studies have shown that ultrasound is a promising tool to detect articular changes such as the presence of osteophytes, and to monitor the progression of OA. Furthermore, 3D ultrasound (3DUS), a tool for volume rendering and surface representation, can potentially offer a means to quantify and depict osteophytes. Objective To compare osteophyte depiction in the knee joint using 3DUS and conventional Computed Tomography (CT) and to evaluate the ability of 3DUS at quantifying osteophyte surface depiction. Methods Eleven fresh‐frozen‐thawed human cadaveric knees were pre‐scanned for the presence of osteophytes according to a previously validated US semi‐quantitative grading system. Five knee sides with visible signs of OA were selected; 3DUS and CT images were obtained, segmented and digitally 3D reconstructed. The knees were dissected and Structured Light Scanner (SLS) images of the physical joint surface were obtained. Using a custom software, surface matching and Root Mean Square Error (RMSE) analyses were performed to assess the accuracy of each of the evaluated modalities in capturing the anatomy of the bone surface at the sites of osteophytes. 3DUS and CT models were compared to the SLS model, which was used as ground truth. Results The average RMSE for 3DUS to SLS and for CT to SLS model comparisons were 0.87 mm and 0.95 mm respectively. No statistical difference was found between 3DUS and CT (p=0.43). Comparative observation of imaging modalities set against each other suggests that 3DUS is superior in depicting osteophytes with cartilage and fibrocartilage tissue characteristics compared to CT. Conclusions 3D Ultrasound can depict features of OA such as osteophytes together with their cartilaginous portion, which is not accurately represented using CT. It is feasible to compare 3DUS to conventional imaging for bone surface depiction within the knee joint. Lastly, 3DUS can provide useful information about not only the presence, but the extent of osteophytes. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".