ArticleLaryngoscope investigative otolaryngology2025
Machine Learning-Based Mobile Application for Predicting Posterior Canal Benign Paroxysmal Positional Vertigo.
Article in Laryngoscope investigative otolaryngology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Current insights on predicting vestibular diseases using machine learning.Turkish journal of medical sciences · 2025Review
- Improving the Diagnostic Workup of Vertigo: A Multidisciplinary Review.Spartan medical research journal · 2025Review
- Artificial intelligence-driven clinical auxiliary diagnosis of benign paroxysmal positional vertigo.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study investigated the predictability of the Posterior Canal Being Paroxysmal Positional Vertigo (PC-BPPV) using vertigo/dizziness features and medical history in machine learning. Secondly, this study aimed to develop a mobile application using the model that predicts PC-BPPV with the highest accuracy rate. Methods: This study retrospectively analyzed the medical records of patients who presented to the Audiology and Balance Clinic with complaints of dizziness or vertigo between 04/01/2021 and 09/16/2023. Patients' diagnoses, demographic information, medical history, and dizziness/vertigo characteristics were used in 8 different machine learning models. A mobile application was developed with the model with the highest accuracy. Results: The study included data from 280 patients. Age, symptom onset time, duration of symptoms, dizziness type, triggering factors, and auditory symptom status were the distinguishing factors for PC-BPPV. Using these features, the Random Forest algorithm predicted PC-BPPV with 96.43% accuracy. The accuracy rates of other algorithms were between 89.28% and 94.64%. Conclusion: Dizziness/vertigo characteristics and medical history can be effectively utilized in machine learning to predict BPPV with high accuracy. The mobile application developed using this algorithm underscores the potential of artificial intelligence platforms to contribute to vestibular science in the telemedicine field. Level of Evidence: Level 2.
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Registered trials
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