Computational Optics for Point-of-Care Breast Cancer Profiling.
Artificial intelligence
Breast cancer
Cellular analysis
Deep-learning algorithms
Global health
Point-of-care diagnostics
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
27
11
2021
pubmed:
28
11
2021
medline:
19
1
2022
Statut:
ppublish
Résumé
With the global burden of cancer on the rise, it is critical to developing new modalities that could detect cancer and guide targeted treatments in fast and inexpensive ways. The need for such technologies is vital, especially in underserved regions where severe diagnostic bottlenecks exist. Recently, we developed a low-cost digital diagnostic system for breast cancer using fine-needle aspirates (FNAs). Named, AIDA (artificial intelligence diffraction analysis), the system combines lens-free digital diffraction imaging with deep-learning algorithms to achieve automated, rapid, and high-throughput cellular analyses for breast cancer diagnosis of FNA and subtype classification for better-guided treatments (Min et al. ACS Nano 12:9081-9090, 2018). Although primarily validated for breast cancer and lymphoma (Min et al. ACS Nano 12:9081-9090, 2018; Im et al. Nat Biomed Eng 2:666-674, 2018), the system could be easily adapted to diagnosing other prevalent cancers and thus find widespread use for global health.
Identifiants
pubmed: 34837178
doi: 10.1007/978-1-0716-1803-5_8
pmc: PMC9283060
mid: NIHMS1821346
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
153-162Subventions
Organisme : NCI NIH HHS
ID : R33 CA202064
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA079443
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA229777
Pays : United States
Organisme : NCI NIH HHS
ID : R00 CA201248
Pays : United States
Organisme : NCI NIH HHS
ID : K99 CA201248
Pays : United States
Organisme : NCI NIH HHS
ID : R21 CA236561
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA257623
Pays : United States
Organisme : NCI NIH HHS
ID : UH3 CA202637
Pays : United States
Organisme : NCI NIH HHS
ID : R21 CA217662
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA233360
Pays : United States
Informations de copyright
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.
Références
Lab Chip. 2016 Apr 21;16(8):1340-5
pubmed: 26980325
Sci Rep. 2016 Apr 21;6:24681
pubmed: 27098438
Proc Natl Acad Sci U S A. 2008 Aug 5;105(31):10670-5
pubmed: 18663227
BMC Res Notes. 2012 Jan 06;5:12
pubmed: 22226127
Nat Biomed Eng. 2018 Sep;2(9):666-674
pubmed: 30555750
Lab Chip. 2011 Apr 7;11(7):1276-9
pubmed: 21365087
Theranostics. 2016 Jun 18;6(10):1603-10
pubmed: 27446494
Breast J. 2015 Jan-Feb;21(1):111-8
pubmed: 25444441
ACS Nano. 2018 Mar 27;12(3):2554-2559
pubmed: 29522316
ACS Nano. 2018 Sep 25;12(9):9081-9090
pubmed: 30113824
Nat Photonics. 2013 Mar 1;7(3):
pubmed: 24358054
Proc Natl Acad Sci U S A. 2015 May 5;112(18):5613-8
pubmed: 25870273
J Vis Exp. 2015 Dec 29;(106):e53180
pubmed: 26780214
Sci Transl Med. 2014 Dec 17;6(267):267ra175
pubmed: 25520396
Nat Nanotechnol. 2011 Apr;6(4):203-15
pubmed: 21441911