Computer-aided detection for tuberculosis and silicosis in chest radiographs of gold miners of South Africa
Bibliographic record
Abstract
BACKGROUND: For over one hundred years, the gold mining sector has been a considerable source of tuberculosis (TB) and silicosis disease burden across Southern Africa. Reading chest radiographs (CXRs) is an expert and time-intensive process necessary for the screening and diagnosis of lung disease and the provision of evidence for compensation claims. Our study explores the use of computer-aided detection (CAD) of TB and silicosis in CXRs of a population with a high incidence of both diseases. METHODS: A set of 330 CXRs with human expert-determined classifications of silicosis, TB, silcotuberculosis and normal were provided to four health technology companies. The ability of each of their respective CAD systems to predict disease was assessed using receiver operating characteristic curve analysis of the under the curve metric. RESULTS: Three of the four systems differentiated accurately between TB and normal images, while two differentiated accurately between silicosis and normal images. Inclusion of silicotuberculosis images reduced each system's ability to detect either disease. In differentiating between any abnormal from normal CXR, the most accurate system achieved both a sensitivity and specificity of 98.2%. CONCLUSION: The current ability of CAD to differentiate between TB and silicosis is limited, but its use as a mass screening tool for both diseases shows considerable promise.
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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) | 0.000 | 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 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".