ArticleSensors (Basel, Switzerland)2024
Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Comprehensive Review of Nystagmus and Vertigo Diagnostics: From Pathological Foundations to AI-Driven Telemedicine.Sensors (Basel, Switzerland) · 2026Review
- Article
- Automated classification of benign paroxysmal positional vertigo from video-nystagmography using a delay-aware neural network.Scientific reports · 2026Article
- Smartphone-based vestibular & gait analysis for remote fall risk assessment.Frontiers in digital health · 2026Article
- The need for speed: using nystagmus velocity profiles and machine learning models to separate canalithiasis BPV from its mimics.Journal of neurology · 2025Article
- Transformer-Based Detection and Clinical Evaluation System for Torsional Nystagmus.Sensors (Basel, Switzerland) · 2025Article
- Impact of head orientation and head movement in traditional manual diagnostics of benign paroxysmal positional vertigo: a randomized controlled crossover study.Frontiers in neurology · 2025Article
- Improving the Diagnostic Workup of Vertigo: A Multidisciplinary Review.Spartan medical research journal · 2025Review
- Article
- Article
- Artificial intelligence-driven clinical auxiliary diagnosis of benign paroxysmal positional vertigo.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystagmus detection models. Among the four deep learning architectures used in this study, the CNN1D model proposed as a nystagmus detection model demonstrated the best performance, exhibiting a sensitivity of 94.06 ± 0.78%, specificity of 86.39 ± 1.31%, precision of 91.34 ± 0.84%, accuracy of 91.02 ± 0.66%, and an
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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.