Determining Thresholds for Computer‐Aided Detection for Silicosis—An Analytic Approach
Bibliographic record
Abstract
BACKGROUND: Computer-aided detection (CAD) is emerging as an adjunct to the use of the chest X-ray (CXR) in screening for pulmonary tuberculosis (TB). CAD for silicosis, a fibrotic lung disease due to silica dust and a strong risk factor for TB, is at an earlier stage of development and, unlike TB, depends on expert human reading for validation. For all CAD systems, an important step is the choice of threshold for classifying images as positive or negative for the disease in question. The objective of this article is to present an analytic approach to the choice of threshold in using CAD systems for silicosis. METHODS: Drawing on receiver operating curve data from a published study on agreement between CAD and two expert readings of silicosis, two criteria for choosing the sensitivity/specificity combination were compared-the Youden Index and a minimum sensitivity of 90%. We explore the impact of criterion selection, silicosis definition, and reader on the choice and interpretation of threshold, as well as the influence of positive predictive value (PPV) derived from screen prevalence. We present a novel technique for using two CAD thresholds to distinguish images with a high likelihood of being of positive or negative from those characterized by uncertainty. RESULTS: The sample was 501 CXR images from ex-gold miners. Derived thresholds varied across the two criteria, as well as across silicosis definition and expert reader. Varying the notional disease prevalence produced large differences in PPV and, therefore, proportions of false positives. The implications of these variations affecting threshold choice are described for three use cases-annual screening of active miners, outreach screening of former miners, and adjudication of claims for silicosis compensation. CONCLUSION: In applying CAD to silicosis, users need to establish the use case, their preference for the sensitivity/specificity trade-off, and the silicosis definition, as well as considering the effect of disease prevalence. System developers need to take inter-reader variation in validation exercises into account and present this information transparently. A two-threshold model has potential utility in situations of high screening volume where there is a significant cost associated with referral for confirmation of diagnosis.
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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 | 0.022 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".