Enhancing breath-based diagnostics through eXplainable Artificial Intelligence
Andrea Lo Sasso, Nicola Amoroso, Domenico Diacono, Marianna La Rocca, Alfonso Monaco, Ester Pantaleo, Sabina Tangaro, Loredana Bellantuono, Roberto Bellotti · 2026
Read Enhancing breath-based diagnostics through eXplainable Artificial Intelligence by Andrea Lo Sasso, Nicola Amoroso, Domenico Diacono, Marianna La Rocca, Alfonso Monaco, Ester Pantaleo, Sabina Tangaro, Loredana Bellantuono, Roberto Bellotti (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/PMC13308859.txt.
Description
Andrea Lo Sasso and co-authors explore in 2026 whether breath tests plus explainable artificial intelligence can flag lung cancer and gut or lung ills early, with no needle or cut. They used open data from three sets, fixed skewed class counts, and checked which breath chemicals drove each best model.
Breath holds volatile chemicals that mirror body state, and pattern tools can sort sick from well on those traces in past work. Open scoring of each chemical weight lets doctors see why a model calls a case as it does. Sample swings, weak sensors, and loose standards still block clinic use today. The frame still gives a sound base for breath based test work ahead.
Catalog record
| CREATOR | Andrea Lo Sasso, Nicola Amoroso, Domenico Diacono, Marianna La Rocca, Alfonso Monaco, Ester Pantaleo, Sabina Tangaro, Loredana Bellantuono, Roberto Bellotti |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52438 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |