Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring
© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature..
Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current analytic software and methods rely on experienced analysts to perform labor-intensive manual inspection and interpretation on a series of 2-dimensional plots via a complex, sequential gating process. With an aggravating shortage of professionals and growing demands, it is very challenging to provide the FC analysis results in a fast, accurate, and reproducible way. Artificial intelligence has been widely used in many sectors to develop automated detection or classification tools. Here we describe a type of machine learning method for developing automated disease classification and residual disease monitoring on clinical flow datasets.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2779 |
|---|---|
| Enthalten in: |
Methods in molecular biology (Clifton, N.J.) - 2779(2024) vom: 01., Seite 353-367 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Wang, Yu-Fen [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Acute myeloid leukemia |
|---|
| Anmerkungen: |
Date Completed 27.03.2024 Date Revised 27.03.2024 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1007/978-1-0716-3738-8_16 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM37016363X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM37016363X | ||
| 003 | DE-627 | ||
| 005 | 20240328000342.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240327s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/978-1-0716-3738-8_16 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1351.xml |
| 035 | |a (DE-627)NLM37016363X | ||
| 035 | |a (NLM)38526794 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Wang, Yu-Fen |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 27.03.2024 | ||
| 500 | |a Date Revised 27.03.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature. | ||
| 520 | |a Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current analytic software and methods rely on experienced analysts to perform labor-intensive manual inspection and interpretation on a series of 2-dimensional plots via a complex, sequential gating process. With an aggravating shortage of professionals and growing demands, it is very challenging to provide the FC analysis results in a fast, accurate, and reproducible way. Artificial intelligence has been widely used in many sectors to develop automated detection or classification tools. Here we describe a type of machine learning method for developing automated disease classification and residual disease monitoring on clinical flow datasets | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Acute myeloid leukemia | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Automated classification | |
| 650 | 4 | |a Flow cytometry | |
| 650 | 4 | |a Machine learning | |
| 700 | 1 | |a Li, Jeng-Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Chi-Chun |e verfasserin |4 aut | |
| 700 | 1 | |a Wallace, Paul K |e verfasserin |4 aut | |
| 700 | 1 | |a Ko, Bor-Sheng |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Methods in molecular biology (Clifton, N.J.) |d 1984 |g 2779(2024) vom: 01., Seite 353-367 |w (DE-627)NLM074849794 |x 1940-6029 |7 nnns |
| 773 | 1 | 8 | |g volume:2779 |g year:2024 |g day:01 |g pages:353-367 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/978-1-0716-3738-8_16 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 2779 |j 2024 |b 01 |h 353-367 | ||