Diagnosing the benign paroxysmal positional vertigo via 1D and deep-learning composite model

Area of Science:

• Neurology
• Artificial Intelligence
• Medical Diagnostics

Background:

• Benign Paroxysmal Positional Vertigo (BPPV) is a primary cause of vertigo.
• Positional maneuvers for BPPV diagnosis generate extensive data, hindering real-time Artificial Intelligence (AI) analysis.
• Current AI diagnostic approaches face challenges due to long data processing times.

Purpose of the Study:

• To develop and evaluate a novel Deep Learning (DL) composite model for rapid and accurate BPPV diagnosis.
• To assess the generalizability of the AI model in real-world clinical settings.
• To identify key features contributing to BPPV classification.

Main Methods:

• A hybrid 1D and DL model was created using data from two distinct patient cohorts.
• Input features included head and eye traces, along with their slow phase velocity (SPV).
• Model performance was evaluated, and a sensitivity study identified the most predictive features.

Main Results:

• The hybrid DL model demonstrated high diagnostic accuracy, with an overall AUROC of 0.982.
• Specific BPPV subtypes showed excellent classification performance, including right posterior (AUROC 0.991) and left posterior (AUROC 0.979).
• The model processed 10 minutes of data in under a second (0.79 ± 0.06 s), with SPV identified as a key predictive feature.

Conclusions:

• The developed DL models enable swift and accurate detection and subtyping of BPPV.
• This AI approach facilitates straightforward clinical diagnosis of BPPV.
• The identification of critical predictive features enhances understanding of BPPV pathophysiology.