Computer-aided detection of pulmonary tuberculosis on digital chest radiographs: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To systematically review the diagnostic accuracy of computer-aided detection (CAD) of pulmonary tuberculosis (PTB) on digital chest radiographs (CXR). DESIGN: We searched four databases for articles published between January 2010 and December 2015 comparing CAD of PTB on CXR to a microbiologic reference standard (smear, culture or polymerase chain reaction). We collected and summarised data on study design, CAD software and diagnostic accuracy (sensitivity, specificity, area under the curve [AUC]). RESULTS: We included 5 of 455 articles identified by searching databases. PTB prevalence ranged from 18% to 60%, and human immunodeficiency virus (HIV) prevalence from 33% to 68%. All articles evaluated CAD4TB, the only commercially available software. AUC ranged from 0.71 to 0.84. Software settings that increased sensitivity resulted in important reductions in specificity, and vice versa. Risk of bias was low in prospective studies (n = 2), and high in retrospective studies (n = 3). CONCLUSION: Evidence assessing CAD's diagnostic accuracy is limited by the small number of studies, most of which have important methodological limitations, the availability and evaluation of only one software programme, and limited generalisability to settings where PTB and HIV are less prevalent. Additional research is required.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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