Rene: A Pre-trained Multi-modal Architecture for Auscultation of Respiratory Diseases
<|reference_start|>Rene: A Pre-trained Multi-modal Architecture for Auscultation of Respiratory Diseases: Compared with invasive examinations that require tissue sampling, respiratory sound testing is a non-invasive examination method that is safer and easier for patients to accept. In this study, we introduce Rene, a pioneering large-scale model tailored for respiratory sound recognition. Rene has been rigorously fine-tuned with an extensive dataset featuring a broad array of respiratory audio samples, targeting disease detection, sound pattern classification, and event identification. Our innovative approach applies a pre-trained speech recognition model to process respiratory sounds, augmented with patient medical records. The resulting multi-modal deep-learning framework addresses interpretability and real-time diagnostic challenges that have hindered previous respiratory-focused models. Benchmark comparisons reveal that Rene significantly outperforms existing models, achieving improvements of 10.27%, 16.15%, 15.29%, and 18.90% in respiratory event detection and audio classification on the SPRSound database. Disease prediction accuracy on the ICBHI database improved by 23% over the baseline in both mean average and harmonic scores. Moreover, we have developed a real-time respiratory sound discrimination system utilizing the Rene architecture. Employing state-of-the-art Edge AI technology, this system enables rapid and accurate responses for respiratory sound auscultation(https://github.com/zpforlove/Rene).<|reference_end|>
@article{zhang2024rene:,
    title={Rene: A Pre-trained Multi-modal Architecture for Auscultation of
  Respiratory Diseases},
    author={Pengfei Zhang, Zhihang Zheng, Shichen Zhang, Minghao Yang and Shaojun
  Tang},
    journal={arXiv preprint arXiv:2405.07442},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07442},
    primaryClass={cs.SD cs.AI eess.AS q-bio.QM}
}