| Title: | Classifying algorithms for SIFT-MS technology and medical diagnosis |
| Author(s): | Moorhead KT; Lee D; Chase JG; Moot AR; Ledingham KM; Scotter J; Allardyce RA; Senthilmohan ST; Endre Z; |
| Address: | "Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. ktm19@student.canterbury.ac.nz" |
| Journal Title: | Comput Methods Programs Biomed |
| DOI: | 10.1016/j.cmpb.2007.11.011 |
| ISSN/ISBN: | 0169-2607 (Print) 0169-2607 (Linking) |
| Abstract: | "Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) is an analytical technique for real-time quantification of trace gases in air or breath samples. SIFT-MS system thus offers unique potential for early, rapid detection of disease states. Identification of volatile organic compound (VOC) masses that contribute strongly towards a successful classification clearly highlights potential new biomarkers. A method utilising kernel density estimates is thus presented for classifying unknown samples. It is validated in a simple known case and a clinical setting before-after dialysis. The simple case with nitrogen in Tedlar bags returned a 100% success rate, as expected. The clinical proof-of-concept with seven tests on one patient had an ROC curve area of 0.89. These results validate the method presented and illustrate the emerging clinical potential of this technology" |
| Keywords: | "*Algorithms Artificial Intelligence Biomarkers Breath Tests/instrumentation/methods Computer Simulation Diagnosis, Computer-Assisted/*methods Gases/*analysis Humans Kidney Kidney Diseases/therapy Nitrogen/chemistry Organic Chemicals/*analysis Pattern Reco;" |
| Notes: | "MedlineMoorhead, K T Lee, D Chase, J G Moot, A R Ledingham, K M Scotter, J Allardyce, R A Senthilmohan, S T Endre, Z eng Research Support, Non-U.S. Gov't Validation Study Ireland 2008/01/12 Comput Methods Programs Biomed. 2008 Mar; 89(3):226-38. doi: 10.1016/j.cmpb.2007.11.011. Epub 2008 Jan 9" |
