Kudo, Shin‐ei

ObjectivesAchalasia 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.MethodsBetween 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.ResultsSensitivity 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.ConclusionCAD 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 registrationThis study was registered in the University Hospital Medical Information Network Clinical Trial Registry (https://www.umin.ac.jp/ctr/) number: UMIN000053047.

The number of artificial intelligence (AI) tools for colonoscopy on the market is increasing with supporting clinical evidence. Nevertheless, their implementation is not going smoothly for a variety of reasons, including lack of data on clinical benefits and cost‐effectiveness, lack of trustworthy guidelines, uncertain indications, and cost for implementation. To address this issue and better guide practitioners, the World Endoscopy Organization (WEO) has provided its perspective about the status of AI in colonoscopy as the position statement. WEO Position Statement: Statement 1.1: Computer‐aided detection (CADe) for colorectal polyps is likely to improve colonoscopy effectiveness by reducing adenoma miss rates and thus increase adenoma detection; Statement 1.2: In the short term, use of CADe is likely to increase health‐care costs by detecting more adenomas; Statement 1.3: In the long term, the increased cost by CADe could be balanced by savings in costs related to cancer treatment (surgery, chemotherapy, palliative care) due to CADe‐related cancer prevention; Statement 1.4: Health‐care delivery systems and authorities should evaluate the cost‐effectiveness of CADe to support its use in clinical practice; Statement 2.1: Computer‐aided diagnosis (CADx) for diminutive polyps (≤5 mm), when it has sufficient accuracy, is expected to reduce health‐care costs by reducing polypectomies, pathological examinations, or both; Statement 2.2: Health‐care delivery systems and authorities should evaluate the cost‐effectiveness of CADx to support its use in clinical practice; Statement 3: We recommend that a broad range of high‐quality cost‐effectiveness research should be undertaken to understand whether AI implementation benefits populations and societies in different health‐care systems.