A Low-Cost Tele-Imaging Platform for Developing Countries
Bibliographic record
Abstract
PURPOSE: To design a "low-cost" tele-imaging method allowing real-time tele-ultrasound expertise, delayed tele-ultrasound diagnosis, and tele-radiology between remote peripherals hospitals and clinics (patient centers) and university hospital centers (expert center). MATERIALS AND METHODS: A system of communication via internet (IP camera and remote access software) enabling transfer of ultrasound videos and images between two centers allows a real-time tele-radiology expertise in the presence of a junior sonographer or radiologist at the patient center. In the absence of a sonographer or radiologist at the patient center, a 3D reconstruction program allows a delayed tele-ultrasound diagnosis with images acquired by a lay operator (e.g., midwife, nurse, technician). The system was tested both with high and low bandwidth. The system can further accommodate non-ultrasound tele-radiology (conventional radiography, mammography, and computer tomography for example). The system was tested on 50 patients between CHR Tsevie in Togo (40 km from Lomé-Togo and 4500 km from Tours-France) and CHU Campus at Lomé and CHU Trousseau in Tours. RESULTS: A real-time tele-expertise was successfully performed with a delay of approximately 1.5 s with an internet bandwidth of around 1 Mbps (IP Camera) and 512 kbps (remote access software). A delayed tele-ultrasound diagnosis was also performed with satisfactory results. The transmission of radiological images from the patient center to the expert center was of adequate quality. Delayed tele-ultrasound and tele-radiology was possible even in the presence of a low-bandwidth internet connection. CONCLUSION: This tele-imaging method, requiring nothing by readily available and inexpensive technology and equipment, offers a major opportunity for telemedicine in developing countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".