Evaluation of a Digital Camera for Acquiring Radiographic Images for Telemedicine Applications
Why this work is in the frame
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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.
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it