Computer-aided Detection of Pulmonary Embolism on CT Angiography
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 evaluate the performance of a computer-aided detection (CAD) system for diagnosis of pulmonary embolism on computed tomography (CT) pulmonary angiography. MATERIALS AND METHODS: One hundred and four pulmonary CT angiograms for pulmonary emboli (PE) were reviewed both by radiologists and a CAD detection system (ImageChecker CT V2.0, R2 Technology Inc, Sunnyvale, CA). CT scans, read and reported by radiologists in a routine daily clinical setting, were later processed by the CAD system. The performance of the CAD system was analyzed. RESULTS: Forty-five PE were identified by the radiologists in 15 patients. The CAD system revealed 123 findings, interpreted by the system as PE. Twenty-six of them, detected in 8 patients, represented true-positive results. Ninety-seven (78.9%) CAD findings were not true PE and were defined as false-positive. Nineteen true PE in 7 patients were missed by the CAD system constituting 42% false-negative rate. Sensitivity of the CAD system was 53.3% and the specificity was 77.5%. The positive predictive value of CAD system was 28.5% and the negative predictive value was 90.7%. CONCLUSIONS: With the evaluated CAD system, it is relatively simple and fast to check all detected findings and decide if they represent true PE. However, high false-negative results demand technologic improvement, to increase the sensitivity of the system. It is anticipated to become a promising supplement to the work and eyes of the radiologist in detecting PE on pulmonary CT angiography.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it