The Use of Computer-Aided Detection for the Assessment of Pulmonary Arterial Filling Defects at Computed Tomographic Angiography
Bibliographic record
Abstract
PURPOSE: To validate a computer-aided detection (CAD) tool for the detection of pulmonary arterial filling defects at computed tomographic pulmonary angiography (CTPA) and to assess its benefit for readers of different levels of experience. METHODS: One hundred consecutive CTPA studies were retrospectively evaluated by a chest radiologist for presence of emboli, serving as the reference standard. Subsequently, examinations were analyzed using commercially available second-generation CAD software (ImageChecker CT, version 2.1; R2 Technology, Inc., Sunnyvale, Calif). The staff radiologist assessed all CAD marks and classified them as true positive or false positive (FP), and any unmarked emboli were classified as false negative. Computer-aided detection software was also evaluated on a case basis compared with the reference standard.For the second part of the study, the 100 CTPAs were reviewed by 3 additional readers of different levels of experience, both without and with CAD, and findings correlated with the reference standard. RESULTS: Twenty-one studies (21%) were positive for pulmonary embolism. Of these, 18 were true positive on a case basis, and 3 were false negative. Of the 79 negative studies, 16 were true negative with no CAD marks, and the remaining 63 were FP. On a case basis, CAD sensitivity was 86%, specificity was 20%, negative predictive value was 84%, and positive predictive value (PPV) was 22%.Overall, the CAD software yielded 318 marks, identifying 64 of 93 emboli with an additional 254 FP marks. On a mark basis, sensitivity was 69%, and PPV was 20%.Computer-aided detection did not influence the most experienced reader (a chest fellow). Although CAD improved the subjective confidence of the second-year resident in some cases, it had no influence on overall interpretation or accuracy. Computer-aided detection improved accuracy only for the most inexperienced reader, helping this reader to identify 9 emboli not initially appreciated. CONCLUSIONS: Computer-aided detection specificity and PPV are poor due to expected FP marks, although, often, these can be easily dismissed. However, CAD software may play an important role as a second reader for residents or inexperienced readers.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".