ArticleClinical and translational gastroenterology2026
Artificial Intelligence-Assisted Confocal Laser Endomicroscopy for Predicting Invasion Depth of Superficial Esophageal Mucosal Lesions: A Cohort Study.
Article in Clinical and translational gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAccurate assessment of invasion depth in esophageal squamous cell carcinoma is essential for optimal treatment. Probe-based confocal laser endomicroscopy (pCLE) enables real-time in vivo imaging, but its interpretation depends heavily on endoscopist expertise. This study aimed to develop and validate an artificial intelligence-assisted pCLE (AI-pCLE) system for the differential diagnosis of low-grade intraepithelial neoplasia (LGIN) from high-grade intraepithelial neoplasia with submucosal invasion (HGIN-SM1).
methodsIn this retrospective single-center study, 1,234 pCLE images were used to develop and validate the AI model. Histopathology from resected specimens served as the reference standard. Six deep learning algorithms were evaluated using accuracy, sensitivity, specificity, area under the curve, positive predictive value (PPV), and negative predictive value (NPV). The best-performing model was selected and compared with 10 endoscopists.
resultsFor the differentiation between LGIN and HGIN-SM1 lesions, the AI-pCLE system exhibited a sensitivity of 97.4%, specificity of 92.6%, accuracy of 95.3%, PPV of 94.17%, and NPV of 96.70%, respectively. In comparison, the mean sensitivity, specificity, accuracy, PPV, and NPV of the endoscopists were 83.79%, 85.26%, 84.46%, 88.14%, and 82.61%, respectively. With AI-pCLE assistance, the accuracy, sensitivity, and NPV of the endoscopists increased to 94.41% ( P = 0.001), 96.98% ( P = 0.006), and 96.24% ( P = 0.001), respectively. The diagnostic performance of the AI-assisted system was comparable with that of expert endoscopists. DISCUSSION: The AI-pCLE system demonstrated robust diagnostic performance in differentiating LGIN from HGIN-SM1 lesions, indicating its potential as a reliable tool for evaluating the invasion depth of esophageal squamous cell carcinoma.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.