Application of Dynamic Time Warping Technique to Evaluate Microstructures of Cancellous Bones
Bibliographic record
Abstract
Osteoporosis is a systemic skeletal disease, which is characterized by the deterioration of microstructures and the decrease of bone mineral density (BMD). Ultrasonic backscatter technique has a promising potential to evaluate microstructures and BMD of cancellous bone for the diagnosis of osteoporosis. The dynamic time warping (DTW)algorithm, measuring the similarity between two signals, has been widely used in speech and image recognition. The purpose of this study is to investigate the feasibility of the DTW algorithm in extracting features from ultrasonic backscatter signals and estimating the microstructure of cancellous bones. Ultrasonic backscatter measurements were performed on twenty-six bovine cancellous bone specimens using an ultrasonic backscatter bone diagnostic system with a focused broadband transducer, which has a center frequency of 1.04 MHz. The dynamic time warped distance (DTWD)feature was extracted from the cost matrix between the ultrasonic backscatter signals and the reference signal. The associations between ultrasonic backscatter features (including DTWD)and trabecular microstructures (i.e., the average trabecular number (Tb. N)and the average trabecular bone spacing (Tb. Sp)), and BMD were analyzed using the simple Pearson's correlations and Partial correlations. The results showed that the DTWD significantly correlated with the Tb.N (R = -0.86±0.09, p <;0.01), the Tb. Sp (R = +0.71±0.12, p <;0.01)and the BMD (R = -0.86±0.08, P <;0.01). After adjustment for the effect of BMD, the microstructure (i.e., Tb. N and Tb. Sp)still yield significant correlations with the DTWD (R = -0.51±0.15, and R = +0.42±0.15, p <;0.01). This study demonstrated that the dynamic time warping technique might have the potential to evaluate the bone microstructure. Ultrasonic backscatter provides a potential measurement for the microstructures of the cancellous bone. Our future work will involve more samples to investigate the efficacy of DTW both in vitro and in vivo.
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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".