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Record W4310502364 · doi:10.1109/ius54386.2022.9957521

AI-Powered Measurement of Ultrasonic Axial-Transmission Velocity for Pediatric Skeletal Development Evaluation

2022· article· en· W4310502364 on OpenAlexaff
Qing Li, Tho N.H.T. Tran, Jialin Guo, Kailiang Xu, Boyi Li, Lawrence H. Le, Dean Ta

Bibliographic record

Venue2022 IEEE International Ultrasonics Symposium (IUS) · 2022
Typearticle
Languageen
FieldEngineering
TopicGait Recognition and Analysis
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsUltrasonic sensorPreprocessorComputer scienceGround truthModality (human–computer interaction)WaveformCluster analysisConsistency (knowledge bases)Fuzzy logicSIGNAL (programming language)Artificial intelligenceComputer visionBiomedical engineeringAcousticsMedicinePhysicsTelecommunications

Abstract

fetched live from OpenAlex

Bone health assessment is commonly used to evaluate skeletal development and maturation and to diagnose neonatal growth disorders. Implementing an automated ultrasonic system for pediatric bone evaluation, which is radiation-free and cost-effective, to complement conventional radiography is significant for clinical applications. This study mainly proposes a method, which combines the ReliefF algorithm with fuzzy C-means clustering, for fully automatic detection of wave arrival time and estimation of ultrasonic velocity from axial transmission waveforms measured from the bone of infants without the need for signal preprocessing. The results of in-vivo velocity measurement show good consistency with the ground truth modality, indicating the feasibility of the proposed method for further clinical studies and usages.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.022
GPT teacher head0.257
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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