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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.08789 |
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| _version_ | 1866913233849286656 |
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| author | Philip, Alexander Chawla, Sanya Jover, Lola Kafentzis, George P. Brew, Joe Saraf, Vishakh Vijayan, Shibu Small, Peter Chaccour, Carlos |
| author_facet | Philip, Alexander Chawla, Sanya Jover, Lola Kafentzis, George P. Brew, Joe Saraf, Vishakh Vijayan, Shibu Small, Peter Chaccour, Carlos |
| contents | Chest X-ray is a commonly used tool during triage, diagnosis and management of respiratory diseases. In resource-constricted settings, optimizing this resource can lead to valuable cost savings for the health care system and the patients as well as to and improvement in consult time. We used prospectively-collected data from 137 patients referred for chest X-ray at the Christian Medical Center and Hospital (CMCH) in Purnia, Bihar, India. Each patient provided at least five coughs while awaiting radiography. Collected cough sounds were analyzed using acoustic AI methods. Cross-validation was done on temporal and spectral features on the cough sounds of each patient. Features were summarized using standard statistical approaches. Three models were developed, tested and compared in their capacity to predict an abnormal result in the chest X-ray. All three methods yielded models that could discriminate to some extent between normal and abnormal with the logistic regression performing best with an area under the receiver operating characteristic curves ranging from 0.7 to 0.78. Despite limitations and its relatively small sample size, this study shows that AI-enabled algorithms can use cough sounds to predict which individuals presenting for chest radiographic examination will have a normal or abnormal results. These results call for expanding this research given the potential optimization of limited health care resources in low- and middle-income countries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08789 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging cough sounds to optimize chest x-ray usage in low-resource settings Philip, Alexander Chawla, Sanya Jover, Lola Kafentzis, George P. Brew, Joe Saraf, Vishakh Vijayan, Shibu Small, Peter Chaccour, Carlos Audio and Speech Processing Artificial Intelligence Machine Learning Quantitative Methods Chest X-ray is a commonly used tool during triage, diagnosis and management of respiratory diseases. In resource-constricted settings, optimizing this resource can lead to valuable cost savings for the health care system and the patients as well as to and improvement in consult time. We used prospectively-collected data from 137 patients referred for chest X-ray at the Christian Medical Center and Hospital (CMCH) in Purnia, Bihar, India. Each patient provided at least five coughs while awaiting radiography. Collected cough sounds were analyzed using acoustic AI methods. Cross-validation was done on temporal and spectral features on the cough sounds of each patient. Features were summarized using standard statistical approaches. Three models were developed, tested and compared in their capacity to predict an abnormal result in the chest X-ray. All three methods yielded models that could discriminate to some extent between normal and abnormal with the logistic regression performing best with an area under the receiver operating characteristic curves ranging from 0.7 to 0.78. Despite limitations and its relatively small sample size, this study shows that AI-enabled algorithms can use cough sounds to predict which individuals presenting for chest radiographic examination will have a normal or abnormal results. These results call for expanding this research given the potential optimization of limited health care resources in low- and middle-income countries. |
| title | Leveraging cough sounds to optimize chest x-ray usage in low-resource settings |
| topic | Audio and Speech Processing Artificial Intelligence Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2402.08789 |