Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening
Bibliographic record
Abstract
Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient's symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".