Evaluation of a Digital Camera for Acquiring Radiographic Images for Telemedicine Applications
Bibliographic record
Abstract
Many rural sites cannot afford a digitizer to digitize radiographic films and transmit them via a telemedicine network for review by a radiology specialist. This project tested the feasibility of using a consumer digital still camera to photograph radiographic images and transmit them via a telemedicine network to a consulting hub site. In this study, the feasibility of using a digital camera to photograph plain film radiographs of 40 bone trauma cases from a rural health center in Arizona was tested. The cases were transmitted to the Arizona Telemedicine Program hub site using a private asynchronous transfer mode network based on T1 carriers. Two orthopedic surgeons and two radiologists reviewed the cases on a color monitor and the original film images. The readers also rated image quality. There were no significant differences in diagnostic accuracy between conventional film and telemedicine reading. Diagnostic agreement between film and monitor viewing was quite high, as was agreement in confidence ratings. Image quality was generally rated as excellent to good in both viewing conditions. Cases that did not correlate well were judged to have poor image quality, or diagnoses were based on photographs that had part of the diagnostic region of interest cropped off. It was determined that a digital still camera can be used effectively in many cases to photograph radiographic images for transmission and viewing via a telemedicine network, as long as adequate views, zoomed in regions of interest, and good quality original films are used in the acquisition process.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".