Can gray values be converted to Hounsfield units? A systematic review
Bibliographic record
Abstract
Objectives: The purpose of this systematic review was to answer the focus question: “Could the gray values (GVs) from CBCT (cone beam computed tomography) be converted to Hounsfield units (HUs) in multidetector computed tomography (MDCT)?” Methods: The included studies try to answer the research question according to the PICO strategy. Studies were gathered by searching several electronic databases and partial grey literature up to January 2021 without language or time restrictions. The methodological assessment of the studies was performed using The Oral Health Assessment Tool (OHAT) for in vitro studies and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) for in vivo studies. The Grading of Recommendations Assessment, Development and Evaluation (GRADE system) instrument was applied to assess the level of evidence across the studies. Results: 2710 articles were obtained in Phase 1, and 623 citations remained after removing duplicates. Only three studies were included in this review using a two-phase selection process and after applying the eligibility criteria. All studies were methodologically acceptable, although in general terms with low risks of bias. There are some included studies with quite low and limited evidence estimations and recommendation forces; evidencing the need for clinical studies with diagnostic capacity to support its use. Conclusions: This systematic review demonstrated that the GVs from CBCT cannot be converted to HUs due to the lack of clinical studies with diagnostic capacity to support its use. However, it is evidenced that three conversion steps (equipment calibration, prediction equation models, and a standard formula (converting GVs to HUs)) are needed to obtain pseudo Hounsfield values instead of only obtaining them from a regression or directly from the software.
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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 | 0.026 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".