  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).
