LLM-GAODE: Large-language-model augmented neural ordinary differential equation network for video nystagmography classification

Benign paroxysmal positional vertigo (BPPV), a common type of vertigo with complex etiologies, is traditionally diagnosed using video nystagmography (VNG). Current automated methods lack diagnostic precision owing to subjective interpretation of eye movement characteristics. To address these challenges, we introduce a l arge l anguage m odel-augmented G ram-based a ttentive neural o rdinary d ifferential e quation ( LLM-GAODE ), an innovative and data-driven framework integrating eye-tracking technology with a Gram-based attention mechanism and a neural ordinary differential equation network to improve BPPV classification. Furthermore, when the neural network exhibits low confidence in its predictions, an LLM can supplement the process with advanced reasoning in natural language. LLM-GAODE was evaluated using an extensive VNG dataset provided by a collaborative university hospital. Results suggest that LLM-GAODE significantly outperforms existing benchmarks in trajectory classification for BPPV diagnosis. The framework enhances BPPV diagnostic accuracy and achieves state-of-the-art performance in open-source trajectory classification benchmarks. The code is available at https://github.com/XiheQiu/LLM-GAODE .