基于机器学习的预测后管良性阵发性位置性眩晕的移动应用

IF 1.7 4区医学 Q2 OTORHINOLARYNGOLOGY

Laryngoscope Investigative Otolaryngology Pub Date : 2025-06-14 DOI:10.1002/lio2.70177

Emre Soylemez, Sait Demir, Kasım Ozacar

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引用次数: 0

摘要

目的利用机器学习中的眩晕/头晕特征和病史，探讨后管为阵发性位置性眩晕（PC-BPPV）的可预测性。其次，本研究旨在利用该模型开发一个预测PC-BPPV准确率最高的移动应用程序。方法回顾性分析2021年4月1日至2023年9月16日在听力学与平衡诊所就诊的以头晕或眩晕为主诉的患者的病历。患者的诊断、人口统计信息、病史和头晕/眩晕特征被用于8种不同的机器学习模型中。利用该模型开发了一个具有最高精度的移动应用程序。结果本研究纳入了280例患者的数据。年龄、症状发生时间、症状持续时间、头晕类型、触发因素、听觉症状状态是PC-BPPV的鉴别因素。利用这些特征，随机森林算法预测PC-BPPV的准确率为96.43%。其他算法的准确率在89.28% ~ 94.64%之间。结论头晕/眩晕特征和病史可以有效地用于机器学习预测BPPV，准确率较高。使用该算法开发的移动应用程序强调了人工智能平台在远程医疗领域为前庭科学做出贡献的潜力。证据等级2级。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Machine Learning-Based Mobile Application for Predicting Posterior Canal Benign Paroxysmal Positional Vertigo

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Machine Learning-Based Mobile Application for Predicting Posterior Canal Benign Paroxysmal Positional Vertigo

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.