SU‐E‐I‐87: Pilot Testing of Software for Automated Remote Quality Control of Digital Mammography Equipment for Use in the Ontario Breast Screening Program
Bibliographic record
Abstract
Purpose: We have evaluated the feasibility of using a software package to monitor the results of quality control (QC) testing on digital mammography units in the Ontario Breast Screening Program. The intent is to make the quality control process more efficient for the technologist and physicist, and improve the consistency of test performance and results interpretation. Methods: A DICOM service class provider, “GLADYS” (originally developed at University Hospitals Leuven) was installed at pilot screening sites. All images acquired on the sitesˈ mammography systems are automatically sent to GLADYS over the PACS networks. GLADYS recognizes QC images by predetermined patient names and performs an automated analysis, measuring various parameters and generating summary thumbnail images. Clinical images are de‐identified and the technique factors and dose are extracted for tracking. The QC and dose reports are sent by email to the central monitoring site. At the central site QC image measures are plotted and thumbnail images displayed for artefact evaluation. The patient header information is stored such that dose reports can be generated. Results: GLADYS has been installed at two remote screening sites, and locally for a total of six machines. QC and dose data have been collected for the past 5.5 months. 285 QC images have been analyzed. Artefacts and changes in automatic exposure control or detector behaviour are easily perceived. Dosimetry information from 18282 patient images has been collected, with an average mean glandular dose of 1.3 mGy. Conclusions: The automated analysis works well, and reduces the technologistˈs QC workload. The addition of features to allow for automated immediate feedback to the remote sites of test results to ensure rapid response to detected problems is under development. Incorporation of centralized automatic quality control has the potential to improve the consistency and reliability of the tests and results, while streamlining QC procedures.
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 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".