ArticleNPJ digital medicine2026
AI Augmented Confocal Laser Endomicroscopy for Rapid Intraoperative Diagnosis of Brain Tumors.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Obstacles and Trials in Treating Primary Brain Tumors.International journal of molecular sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Intraoperative diagnosis is important for brain tumor surgery. Although frozen section (FS) remains the standard, its time demands and logistical limitations hinder real-time decision-making. Confocal laser endomicroscopy (CLE) offers rapid high-resolution imaging for efficient diagnosis. This multicenter prospective trial evaluated the diagnostic performance of CLE while developing a novel Swin Transformer-based AI diagnostic model. A total of 461 biopsies from 376 patients were analyzed. CLE demonstrated non-inferior diagnostic accuracy to FS (0.94 vs 0.92; P = 0.14), with comparable sensitivity (0.96 vs 0.95; P = 0.40) and specificity (0.79 vs 0.68; P = 0.31). CLE achieved a significantly faster median turnaround (5 m 56 s vs 20 m; P < 0.001). The AI model showed diagnostic accuracies of 0.94 for tumor detection and 0.88 for biopsy subtype diagnosis. Overall, CLE demonstrated diagnostic efficacy comparable to FS with significantly shorter turnaround. The AI diagnostic model also showed promising results supporting its potential integration with the CLE platform.
Identifiers
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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.