A Clinical Tool for Automated Flow Cytometry Based on Machine Learning Methods
| Autor: | Erling Si, Martin Kampel, Claude Takenga, Melanie Gau, Markus Diem, Michael Brandstoetter, Michael Dworzak, Rolf-Dietrich Berndt, Leonid Karawajew |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Telemedicine medicine.diagnostic_test Computer science business.industry Becton dickinson Decision rule Machine learning computer.software_genre Minimal residual disease Flow cytometry 03 medical and health sciences 030104 developmental biology Feature (computer vision) medicine Artificial intelligence business Throughput (business) computer Statistical hypothesis testing |
| Zdroj: | Bioinformatics and Biomedical Engineering ISBN: 9783319561530 IWBBIO (2) |
| DOI: | 10.1007/978-3-319-56154-7_48 |
| Popis: | Clinical researchers working in flow cytometry (FCM) nowadays experience increasing demands to perform experiments that involve high throughput, rare event analysis and detailed immunophenotyping. Beckman Coulter and Becton Dickinson offer multi-use flow cytometry sorters that can analyze up to 70K EPS (events per seconds) with more than nine parameters enabled. While this multi-parametric feature provides a great power for hypothesis testing, it also generates a vast amount of data, which is analyzed manually through a processing called gating. For large experiments, this manual gating turns out to be time consuming and requires intensive operator training and experience. The lack of required expertise leads to wrong interpretation of data, thus a wrong therapy course for the case of patients with acute lymphoblastic leukemia (ALL) is followed. This paper aims to present a pipeline-software, as a ready-to-use machine learning based automated FCM assessment tool for the daily clinical practice for patients with ALL. The new system increases accuracy in assessment of FCM based on minimal residual disease (MRD) method in samples analyzed by conventional operator-based gating since computer-aided analysis potentially has a higher power due to the use of the whole multi-parametric FCM-data space at once instead of using methods restricted to two-dimensional decision rules. The tool is implemented as a telemedical network for analysis, clinical follow-up, treatment monitoring of leukemia and allows dissemination of automated FCM-MRD analysis to medical centres in the world. |
| Databáze: | OpenAIRE |
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