Interpretable machine learning methods based on oscillometry and electric modeling for the diagnostic of respiratory dysfunction in silicosis
Jorge Luís Machado do Amaral, Cíntia Moraes de Sá Sousa, Caroline de Oliveira Ribeiro, Paula Morisco de Sá, Agnaldo José Lopes, Pedro Lopes de Melo · 2026
Read Interpretable machine learning methods based on oscillometry and electric modeling for the diagnostic of respiratory dysfunction in silicosis by Jorge Luís Machado do Amaral, Cíntia Moraes de Sá Sousa, Caroline de Oliveira Ribeiro, Paula Morisco de Sá, Agnaldo José Lopes, Pedro Lopes de Melo (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13343688.txt.
Description
Silicosis, the lung scarring left by years of breathing mineral dust, is hard to grade with simple breath tests. Jorge Luís Machado do Amaral and co-authors bring in sound-based breath measures plus learning models in a 2026 study. Volunteers breathed into a device that reads how airways answer tiny pressure waves, and those readings fed several types of model.
One plain reading, the pitch where the airway system resonates, already split sick from healthy fairly well. Models did better, and the best reached high scores on held-out and fresh test groups. One model type also shows which readings drive its call and how they interact, a rare plus in medical AI. The team offers the setup as a support tool for clinics that screen dust workers.
Catalog record
| CREATOR | Jorge Luís Machado do Amaral, Cíntia Moraes de Sá Sousa, Caroline de Oliveira Ribeiro, Paula Morisco de Sá, Agnaldo José Lopes, Pedro Lopes de Melo |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |