# Rene: A Pre-trained Multi-modal Architecture for Auscultation of Respiratory Diseases

arXiv: https://arxiv.org/abs/2405.07442 · HTML: https://mlsanity.com/paper/2405.07442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 15 of 15)

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- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Using the Output Embedding to Improve Language Models](https://mlsanity.com/paper/1608.05859/) (2016)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1](https://mlsanity.com/paper/2102.03049/) (2021)
- [Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases](https://mlsanity.com/paper/2002.03894/) (2020)

## Abstract (arXiv)

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).