Computer‐aided detection for esophageal achalasia (with video)
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
OBJECTIVES: Achalasia is an esophageal motility disorder that impairs quality of life and is often missed (20-50%) on endoscopy. A newly developed computer-aided detection (CAD) software has shown high accuracy for achalasia diagnosis in preclinical settings. However, its benefit in a clinical setting remains unclear. METHODS: Between February and August 2023, 83 endoscopists from 27 centers assessed 50 randomized endoscopic videos (25 achalasia, 25 nonachalasia) without and with CAD. Endoscopists assessed videos without CAD, then with CAD after 2 months. The primary end-point was improvement in sensitivity for nonexperienced endoscopists (no endoscopic experience of achalasia). Sensitivity, specificity, and accuracy with and without CAD were compared using the McNemar test. RESULTS: Sensitivity for diagnosing achalasia increased significantly with CAD, rising from 74.2% (95% confidence interval [CI] 72.2-76.0%) to 91.2% (95% CI 89.9-92.4%) for all readers, showing a difference of 17.1% (95% CI 15.1-19.0%). Specifically, sensitivity improved from 66.9% (95% CI 63.6-70.0%) to 91.9% (95% CI 89.9-93.6%) among nonexperienced endoscopists, resulting in a difference of 25.0% (95% CI 21.7-28.4%), and from 79.5% (95% CI 77.1-81.8%) to 90.8% (95% CI 89.0-92.3%) among experienced endoscopists (endoscopic experience of at least one achalasia case), with a difference of 11.3% (95% CI 8.9-13.6%). Accuracy and specificity improved significantly with CAD assistance, regardless of reader's experience. CONCLUSION: CAD improves achalasia detection by 17%, confirming preclinical results. The benefit was higher for nonexperienced endoscopists. CAD assistance may lead to prompt and effective treatment, minimizing the risk of false-negative diagnosis in clinical practice. TRIAL REGISTRATION: This study was registered in the University Hospital Medical Information Network Clinical Trial Registry (https://www.umin.ac.jp/ctr/) number: UMIN000053047.
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 | 0.000 | 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 it