ReviewFrontiers in neurology
Artificial intelligence-driven clinical auxiliary diagnosis of benign paroxysmal positional vertigo.
Review in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Comprehensive Review of Nystagmus and Vertigo Diagnostics: From Pathological Foundations to AI-Driven Telemedicine.Sensors (Basel, Switzerland) · 2026Review
- Automated classification of benign paroxysmal positional vertigo from video-nystagmography using a delay-aware neural network.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Benign paroxysmal positional vertigo (BPPV) is one of the most prevalent peripheral vertigo disorders in clinical practice. Its definitive diagnosis relies heavily on characteristic nystagmus induced by positional provocative tests, which imposes high requirements on clinicians and is subject to limitations such as strong subjectivity. The breakthrough advances in artificial intelligence (AI) technologies have provided innovative solutions for the accurate diagnosis and personalized treatment of BPPV. This review systematically summarizes the research progress of AI in the clinical application of BPPV, its enormous potential to improve BPPV diagnostic efficacy, and future directions for development.
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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.