Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules
Authors: Morad K. NakhlehHaitham AmalRaneen JeriesYoav Y. BrozaManal AboudAlaa GharraHodaya IvgiSalam KhatibShifaa BadarnehLior Har-ShaiLea Glass-MarmorIzabella LejbkowiczAriel MillerSamih BadarnyRaz WinerJohn FinbergSylvia Cohen-KaminskyFrédéric PerrosDavid MontaniBarbara GirerdGilles GarciaGérald SimonneauFarid NakhoulShira BaramRaed SalimMarwan HakimMaayan GruberOhad RonenTal MarshakIlana DoweckOfer NativZaher BahouthDa-you ShiWei ZhangQing-ling HuaYue-yin PanLi TaoHu LiuAmir KarbanEduard KoifmanTova RainisRoberts SkaparsArmands SivinsGuntis AncansInta Liepniece-KareleIlze KikusteIeva LasinaIvars TolmanisDouglas JohnsonStuart Z. MillstoneJennifer FultonJohn W. WellsLarry H. WilfMarc HumbertMarcis LejaNir PeledHossam Haick
This paper presents a new method to identify 17 different diseases (cancerous, inflammatory and neurological ones) with high accuracy from human’s exhaled breath. An artificially intelligent nanoarray has been built consisting of organic and inorganic sensors to detect volatile organic compounds (VOCs) from breath and transform them to electrical parameters. Each disease has its unique volatile molecular print based on different amounts of 13 components in the breath. An overall probability of 86% was achieved for the accuracy during the clinical trials.
Experiments were carried out in 5 countries at 9 clinical facilities on a total of 1404 patients. 813 patients were suffering from either of the 17 mentioned diseases, the others were healthy. Two breath samples were taken from each patient, one was captured and processed by the nanoarray, the other was analysed by mass spectrometry for validation purposes. The results were blinded, so researchers did not know which condition the participants had. It was possible to obtain that the results became independent from several factors, like gender, age, smoking habits and geographic location. It is also very important to mention that one disease did not prevent the detection of the others.
This new method can be a base of developing inexpensive portable diagnostic devices to identify several human diseases in advance, even at home.
Read the original article here: ACS Publications